All Papers

• "Is ChatGPT a Better Explainer than My Professor?": Evaluating the Explanation Capabilities of LLMs in Conversation Compared to a Human Baseline
• "It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systems
• "My Boyfriend is AI": A Computational Analysis of Human-AI Companionship in Reddit's AI Community
• (QA)2: Question Answering with Questionable Assumptions
• 1. ELI5 (Explain Like I'm 5)
• 100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models
• 1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
• 12 New Advanced Types of RAG
• 2. TL;DR (Summarize Long Text)
• 3. Jargonize (Professional/Nerdy Tone)
• 4. Humanize (Sound More Natural)
• 5. Feynman Technique (Deep Understanding)
• A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy
• A Comment On "The Illusion of Thinking": Reframing the Reasoning Cliff as an Agentic Gap
• A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
• A comprehensive analysis of concept drift locality in data streams
• A Comprehensive Evaluation of Inductive Reasoning Capabilities and Problem Solving in Large Language Models
• A Comprehensive Review of AI-based Intelligent Tutoring Systems: Applications and Challenges
• A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications
• A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
• A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
• A comprehensive taxonomy of hallucinations in Large Language Models
• A Computational Framework for Behavioral Assessment of LLM Therapists
• A Contextual-Bandit Approach to Personalized News Article Recommendation
• A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems
• A Decomposition Perspective to Long-context Reasoning for LLMs
• A Domain Specific Modeling Language for Multiagent Systems
• A Few Words Can Distort Graphs: Knowledge Poisoning Attacks on Graph-based Retrieval-Augmented Generation of Large Language Models
• A Framework for Collaborating a Large Language Model Tool in Brainstorming for Triggering Creative Thoughts
• A Hybrid Human-AI Approach for Argument Map Creation From Transcripts
• A Hybrid Intelligence Method for Argument Mining
• A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning
• A Little Human Data Goes A Long Way
• A Looming Replication Crisis in Evaluating Behavior in Language Models? Evidence and Solutions
• A Mechanistic Interpretation of Arithmetic Reasoning in Language Models using Causal Mediation Analysis
• A meta-analysis of the persuasive power of large language models
• A Multi-facet Paradigm to Bridge Large Language Model and Recommendation
• A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity
• A natural language processing approach reveals first-person pronoun usage and non-fluency as markers of therapeutic alliance in psychotherapy
• A Non-Factoid Question-Answering Taxonomy
• A Personalized Recommender System based-on Knowledge Graph Embeddings
• A polar coordinate system represents syntax in large language models
• A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows
• A Probabilistic Model for Using Social Networks in Personalized Item Recommendation
• A recipe for annotating grounded clarifications
• A ripple in time: a discontinuity in American history
• A Robustness Evaluation Framework for Argument Mining
• A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations
• A sociotechnical perspective for the future of AI: narratives, inequalities, and human control
• A Survey of Calibration Process for Black-Box LLMs
• A Survey of Continual Reinforcement Learning
• A Survey of Meta-Reinforcement Learning
• A Survey of Reinforcement Learning from Human Feedback
• A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence
• A Survey on Concept Drift Adaptation
• A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directions
• A Survey on Diffusion Language Models
• A Survey on Knowledge Distillation of Large Language Models
• A Survey on Large Language Models for Recommendation
• A Survey on Large Language Models with some Insights on their Capabilities and Limitations
• A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation
• A Survey on LLM Inference-Time Self-Improvement
• A Survey on Post-training of Large Language Models
• A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects
• A Survey on Prompt Tuning
• A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?
• A Systematic Review on the Evaluation of Large Language Models in Theory of Mind Tasks
• A Taxonomy of Empathetic Questions in Social Dialogs
• A Tutorial on LLM Reasoning: Relevant Methods behind ChatGPT o1
• A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems
• Abductive Reasoning with the GPT-4 Language Model: Case studies from criminal investigation, medical practice, scientific research
• Abg-CoQA: Clarifying Ambiguity in Conversational Question Answering
• Absolute Zero: Reinforced Self-play Reasoning with Zero Data
• AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions
• ACE: Abstractions for Communicating Efficiently
• Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems
• Activation Steering for Chain-of-Thought Compression
• Active Listening: Personalized Question Generation in Open-Domain Social Conversation with User Model Based Prompting
• Active Retrieval Augmented Generation
• Adam's Law: Textual Frequency Law on Large Language Models
• Adaptation of Agentic AI
• Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization
• Adapting LLM Agents with Universal Feedback in Communication
• Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics
• Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home
• Adding Chit-Chat to Enhance Task-Oriented Dialogues
• Advances and Challenges in Conversational Recommender Systems: A Survey
• Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling
• Aether Weaver: Multimodal Affective Narrative Co-Generation with Dynamic Scene Graphs
• Affordable AI Assistants with Knowledge Graph of Thoughts
• Agent Development Kit
• Agent Laboratory: Using LLM Agents as Research Assistants
• Agent Learning via Early Experience
• Agent S: An Open Agentic Framework that Uses Computers Like a Human
• Agent Workflow Memory
• Agent-as-a-Judge: Evaluate Agents with Agents
• Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models
• Agent-R: Training Language Model Agents to Reflect via Iterative Self-Training
• AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
• AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation
• Agentic AI and the next intelligence explosion
• Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
• Agentic Reasoning for Large Language Models
• Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research
• Agentic Web: Weaving the Next Web with AI Agents
• AgentRxiv: Towards Collaborative Autonomous Research
• Agents Are Not Enough
• Agents of Chaos
• AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
• AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents
• Agreement Tracking for Multi-Issue Negotiation Dialogues
• AI & Human Co-Improvement for Safer Co-Superintelligence
• AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
• AI Assistance Reduces Persistence and Hurts Independent Performance
• AI Can Learn Scientific Taste
• AI Enters Public Discourse: A Habermasian Assessment Of The Moral Status Of Large Language Models
• AI Meets the Classroom: When Does ChatGPT Harm Learning?
• AI Models Exceed Individual Human Accuracy in Predicting Everyday Social Norms
• AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting
• AI-Powered (Finance) Scholarship
• AI-Researcher: Autonomous Scientific Innovation
• AInsight: Augmenting Expert Decision-Making with On-the-Fly Insights Grounded in Historical Data
• Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models
• Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models
• ALIGN: Prompt-based Attribute Alignment for Reliable, Responsible, and Personalized LLM-based Decision-Making
• Aligning Language Models to Explicitly Handle Ambiguity
• Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning
• All AI Models are Wrong, but Some are Optimal
• All AI PDFs
• AlphaGo Moment for Model Architecture Discovery
• Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models
• An Automatic Graph Construction Framework based on Large Language Models for Recommendation
• An Emulator for Fine-Tuning Large Language Models using Small Language Models
• An extended framework for characterizing social robots
• An Investigation of Robustness of LLMs in Mathematical Reasoning: Benchmarking with Mathematically-Equivalent Transformation of Advanced Mathematical Problems
• An Overview Of Temporal Commonsense Reasoning and Acquisition
• ANAPHORA RESOLUTION: THE STATE OF THE ART
• Answer is All You Need: Instruction-following Text Embedding via Answering the Question
• Answering Questions by Meta-Reasoning over Multiple Chains of Thought
• Apollo's Oracle: Retrieval-Augmented Reasoning in Multi-Agent Debates
• Are Customers Lying to Your Chatbot?
• Are Emergent Abilities in Large Language Models just In-Context Learning?
• Are Emergent Abilities of Large Language Models a Mirage?
• Are LLMs All You Need for Task-Oriented Dialogue?
• Are you in a Masquerade? Exploring the Behavior and Impact of Large Language Model Driven Social Bots in Online Social Networks
• AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning
• ARGS: Alignment as Reward-Guided Search
• Argument Quality Assessment in the Age of Instruction-Following Large Language Models
• Argument Summarization and its Evaluation in the Era of Large Language Models
• Argumentative Large Language Models for Explainable and Contestable Decision-Making
• Argunauts: Open LLMs that Master Argument Analysis with Argdown
• Arithmetic Without Algorithms: Language Models Solve Math With a Bag of Heuristics
• Artifacts as Memory Beyond the Agent Boundary
• Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)
• Artificial Intelligence and the Labor Market∗
• Artificial intelligence is ineffective and potentially harmful for fact checking
• Ask an Expert: Leveraging Language Models to Improve Strategic Reasoning in Goal-Oriented Dialogue Models
• Ask, and it shall be given: Turing completeness of prompting
• Ask-AC: An Initiative Advisor-in-the-Loop Actor-Critic Framework
• Asking Clarifying Questions Based on Negative Feedback in Conversational Search
• Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification
• Assessing adaptive world models in machines with novel games
• Assessing the Ability of ChatGPT to Screen Articles for Systematic Reviews
• Assessment of Personality Dimensions Across Situations Using Conversational Speech
• Atesa-bært: A Heterogeneous Ensemble Learning Model For Aspect-based Sentiment Analysis
• Atom of Thoughts for Markov LLM Test-Time Scaling
• Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward
• Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Data
• Attention on the brain
• Attention, Intentions, And The Structure Of Discourse
• Attentive Reasoning Queries: A Systematic Method for Optimizing Instruction-Following in Large Language Models
• Attribute Controlled Dialogue Prompting
• Auditing language models for hidden objectives
• Augmenting Autotelic Agents with Large Language Models
• Augmenting Netflix Search with In-Session Adapted Recommendations
• AutoCBT: An Autonomous Multi-agent Framework for Cognitive Behavioral Therapy in Psychological Counseling
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
• AutoGLM: Autonomous Foundation Agents for GUIs
• Automated Alignment Researchers: Using large language models to scale scalable oversight
• Automated Design of Agentic Systems
• Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation
• Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data
• Automatic Prompt Optimization with "Gradient Descent" and Beam Search
• Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies
• AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
• Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey
• AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders
• Backtracing: Retrieving the Cause of the Query
• Base Models Know How to Reason, Thinking Models Learn When
• Behavioral Exploration: Learning to Explore via In-Context Adaptation
• Benchmarking Floworks against OpenAI & Anthropic: A Novel Framework for Enhanced LLM Function Calling
• Benchmarking the Pedagogical Knowledge of Large Language Models
• Better Alignment with Instruction Back-and-Forth Translation
• Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases
• Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs
• Beyond "Not Novel Enough": Enriching Scholarly Critique with LLM-Assisted Feedback
• Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey
• Beyond Accuracy: The Role of Calibration in Self-Improving Large Language Models
• Beyond Answers: How LLMs Can Pursue Strategic Thinking in Education
• Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty
• Beyond Brainstorming: What Drives High-Quality Scientific Ideas? Lessons from Multi-Agent Collaboration
• Beyond Context Limits: Subconscious Threads for Long-Horizon Reasoning
• Beyond Discrete Personas: Personality Modeling Through Journal Intensive Conversations
• Beyond GPT-5: Making LLMs Cheaper and Better via Performance-Efficiency Optimized Routing
• Beyond Hallucinations: The Illusion of Understanding in Large Language Models
• Beyond neural scaling laws: beating power law scaling via data pruning
• Beyond Passive Critical Thinking: Fostering Proactive Questioning to Enhance Human-AI Collaboration
• Beyond Preferences in AI Alignment
• Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts
• Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment
• Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning
• Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens
• Beyond Single Models: Enhancing LLM Detection of Ambiguity in Requests through Debate
• Beyond Ten Turns: Unlocking Long-Horizon Agentic Search with Large-Scale Asynchronous RL
• Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
• Beyond the Exploration-Exploitation Trade-off: A Hidden State Approach for LLM Reasoning in RLVR
• Beyond the Last Answer: Your Reasoning Trace Uncovers More than You Think
• Beyond the Surface: Probing the Ideological Depth of Large Language Models
• Beyond the Trade-off: Self-Supervised Reinforcement Learning for Reasoning Models' Instruction Following
• Beyond Turing: Memory-Amortized Inference as a Foundation for Cognitive Computation
• Bigger is not always better: The importance of human-scale language modeling for psycholinguistics
• Bilevel Autoresearch: Meta-Autoresearching Itself
• Boosted Prompt Ensembles for Large Language Models
• Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought
• Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Need
• Boundless Socratic Learning with Language Games
• Bounds of Chain-of-Thought Robustness: Reasoning Steps, Embed Norms, and Beyond
• Branch-Solve-Merge Improves Large Language Model Evaluation and Generation
• Break It Down: Evidence for Structural Compositionality in Neural Networks
• Break the Chain: Large Language Models Can be Shortcut Reasoners
• Bridging Offline and Online Reinforcement Learning for LLMs
• Bridging the gulf of envisioning: Cognitive design challenges in llm interfaces.
• Building a Stronger CASA: Extending the Computers Are Social Actors Paradigm
• Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions
• Building Cooperative Embodied Agents Modularly with Large Language Models
• Building Decision Making Models Through Language Model Regime
• Building Machines that Learn and Think with People
• Building Persona Consistent Dialogue Agents with Offline Reinforcement Learning
• Byte Latent Transformer:  Patches Scale Better Than Tokens
• Calibrated Recommendations
• CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
• Can AI Explanations Make You Change Your Mind?
• Can AI Have a Personality? Prompt Engineering for AI Personality Simulation: A Chatbot Case Study in Gender-Affirming Voice Therapy Training
• Can Authorship Representation Learning Capture Stylistic Features?
• Can Language Models Recognize Convincing Arguments?
• Can Language Models Represent the Past without Anachronism?
• Can Language Models Serve as Text-Based World Simulators?
• Can Language Models Solve Graph Problems in Natural Language?
• Can Large Language Models Capture Human Annotator Disagreements?
• Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess
• Can Large Language Models do Analytical Reasoning?
• Can large language models explore in-context?
• Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability to Mark Short Answer Questions in K-12 Education
• Can Large Language Models perform Relation-based Argument Mining?
• Can Large Language Models Really Improve by Self-critiquing Their Own Plans?
• Can Large Language Models Reason and Plan?
• Can Large Language Models Transform Computational Social Science?
• Can Large Language Models Understand Context?
• Can Large Reasoning Models Self-Train?
• Can LLM be a Personalized Judge?
• Can LLMs assist with Ambiguity? A Quantitative Evaluation of various Large Language Models on Word Sense Disambiguation
• Can LLMs Follow Simple Rules?
• Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
• Can LLMs Ground when they (Don't) Know: A Study on Direct and Loaded Political Questions
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?
• Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?
• Can Machines Think Like Humans? A Behavioral Evaluation of LLM-Agents in Dictator Games
• Can robots do therapy?: Examining the efficacy of a CBT bot in comparison with other behavioral intervention technologies in alleviating mental health symptoms
• Can Theoretical Physics Research Benefit from Language Agents?
• Can You Trust LLM Judgments? Reliability of LLM-as-a-Judge
• CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues
• Cats Confuse Reasoning LLM: Query Agnostic Adversarial Triggers for Reasoning Models
• Causal Claims in Economics
• Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
• CDW-CoT: Clustered Distance-Weighted Chain-of-Thoughts Reasoning
• CEO: Corpus-based Open-Domain Event Ontology Induction
• Chain of Draft: Thinking Faster by Writing Less
• Chain of Stance: Stance Detection with Large Language Models
• Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
• Chain of Thoughtlessness? An Analysis of CoT in Planning
• Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
• Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering
• Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective
• Chain-of-Retrieval Augmented Generation
• Chain-of-Thought Is Not Explainability
• Chain-of-thought Reasoning Is A Policy Improvement Operator
• Chain-of-Thought Reasoning Without Prompting
• Chain-of-Verification Reduces Hallucination in Large Language Models
• Challenges of Large Language Models for Mental Health Counseling
• Chamain: Harmonizing Character Persona Integrity with Domain-Adaptive Knowledge in Dialogue Generation
• Character is Destiny: Can Role-Playing Language Agents Make Persona-Driven Decisions?
• Characterizing Deep Research: A Benchmark and Formal Definition
• Characterizing Online Discussion Using Coarse Discourse Sequences
• Chatbot vs. Human: The Impact of Responsive Conversational Features on Users’ Responses  to Chat Advisors
• Chatbots in Knowledge-Intensive Contexts: Comparing Intent and LLM-Based Systems
• ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
• ChatGPT codes
• ChatGPT Doesn’t Trust Chargers Fans: Guardrail Sensitivity in Context
• ChatGPT is not Enough: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling
• ChatGPT Reads Your Tone and Responds Accordingly -- Until It Does Not -- Emotional Framing Induces Bias in LLM Outputs
• ChatGPT: deconstructing the debate and moving it forward
• ChatGPT: towards AI subjectivity
• Checklists Are Better Than Reward Models For Aligning Language Models
• Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems
• Circuit Tracing: Revealing Computational Graphs in Language Models
• CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning
• Clarifying the Path to User Satisfaction: An Investigation into Clarification Usefulness
• Classifying YouTube Comments Based on Sentiment and Type of Sentence
• Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data
• CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization
• CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models
• Clustering-based Sampling for Few-Shot Cross-Domain Keyphrase Extraction
• CogBench: a large language model walks into a psychology lab
• Cognitive Architectures for Language Agents
• Cognitive Chain-of-Thought: Structured Multimodal Reasoning about Social Situations
• Cognitive Effects in Large Language Models
• CollabLLM: From Passive Responders to Active Collaborators
• Collaborative Deep Learning for Recommender Systems
• Collaborative Filtering Bandits
• Collaborative Filtering for Implicit Feedback Datasets
• Collaborative Filtering with Temporal Dynamics
• Collaborative Rational Speech Act: Pragmatic Reasoning for Multi-Turn Dialog
• Collaborative Reasoner: Self-Improving Social Agents with Synthetic Conversations
• CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation
• Comment on The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
• ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning
• Comparing Apples to Apples: Generating Aspect-Aware Comparative Sentences from User Reviews
• Comparing emotion feature extraction approaches for predicting depression and anxiety
• Comparing Human and AI Therapists in Behavioral Activation for Depression: Cross-Sectional Questionnaire Study
• COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies with Language Modeling
• Competitive Programming with Large Reasoning Models
• Complex Logical Instruction Generation
• Complexity-Based Prompting for Multi-Step Reasoning
• Compositional Reasoning with Transformers, RNNs, and Chain of Thought
• Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning
• Compress to Impress: Unleashing the Potential of Compressive Memory in Real-World Long-Term Conversations
• Computational Modelling of Undercuts in Real-world Arguments
• Computational structuralism: Toward a formal theory of meaning in the age of digital intelligence
• Computer says “No”: The Case Against Empathetic Conversational AI
• Conceptual Design Generation Using Large Language Models
• Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data
• CONSCENDI: A Contrastive and Scenario-Guided Distillation Approach to Guardrail Models for Virtual Assistants
• Considering the Context to Build Theory in HCI, HRI, and HMC: Explicating Differences in Processes of Communication and Socialization With Social Technologies
• Consistency Training Helps Stop Sycophancy and Jailbreaks
• Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendations
• Consistently Simulating Human Personas with Multi-Turn Reinforcement Learning
• Content-aware Collaborative Music Recommendation Using Pre-trained Neural Networks
• Context Embeddings for Efficient Answer Generation in RAG
• Context Tuning for Retrieval Augmented Generation
• Context-PEFT: Efficient Multi-Modal, Multi-Task Fine-Tuning
• Continual Instruction Tuning for Large Multimodal Models
• CONTROL PREFIXES for Parameter-Efficient Text Generation
• Controlling Linguistic Style Aspects in Neural Language Generation
• Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents
• Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations
• Conversation Derailment Forecasting with Graph Convolutional Networks
• Conversational Alignment with Artificial Intelligence in Context
• Conversational DNA: A New Visual Language for Understanding Dialogue Structure in Human and AI
• Conversational Graph Grounded Policy Learning for Open-Domain Conversation Generation
• Conversational Prompt Engineering
• Conversational Recommendation: A Grand AI Challenge
• Conversational Semantic Parsing for Dialog State Tracking
• Conversations Gone Awry: Detecting Early Signs of Conversational Failure
• CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
• CoT is Not True Reasoning, It Is Just a Tight Constraint to Imitate: A Theory Perspective
• CoT-Self-Instruct: Building high-quality synthetic prompts for reasoning and non-reasoning tasks
• Could you be wrong: Debiasing LLMs using a metacognitive prompt for improving human decision making
• Creativity Has Left the Chat: The Price of Debiasing Language Models
• Critical-Questions-of-Thought: Steering LLM reasoning with Argumentative Querying
• Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate
• Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback
• Critiques of World Models
• CRMArena-Pro: Holistic Assessment of LLM Agents Across Diverse Business Scenarios and Interactions
• Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domains
• Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs
• Cultural Evolution of Cooperation among LLM Agents
• Cumulated Gain-Based Evaluation of IR Techniques
• Cumulative Reasoning with Large Language Models
• CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
• Curse of “Low” Dimensionality in Recommender Systems
• DAPIE:  Interactive  Step-by-Step  Explanatory  Dialogues  to  Answer  Children’s  Why  and  How  Questions
• DAPO: An Open-Source LLM Reinforcement Learning System at Scale
• Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
• DataComp-LM: In search of the next generation of training sets for language models
• DATATALES: Investigating the use of Large Language Models for Authoring Data-Driven Articles
• Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models
• DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations
• Debating with More Persuasive LLMs Leads to More Truthful Answers
• Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning
• Decision Transformer: Reinforcement Learning via Sequence Modeling
• Decision-Oriented Dialogue for Human–AI Collaboration
• Decomposed Prompting: A Modular Approach for Solving Complex Tasks
• Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory
• DEEM: Dynamic Experienced Expert Modeling for Stance Detection
• Deep Interest Network for Click-Through Rate Prediction
• Deep Language Networks: Joint Prompt Training of Stacked LLMs using Variational Inference
• Deep Neural Network Approach for the Dialog State Tracking Challenge
• Deep Neural Networks for YouTube Recommendations
• Deep Research: A Systematic Survey
• Deep Researcher with Test-Time Diffusion
• Deep Think with Confidence
• DeepCT-enhanced Lexical Argument Retrieval
• DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL
• DeepGesture: A conversational gesture synthesis system based on emotions and semantics
• DeepNet: Scaling Transformers to 1,000 Layers
• DeepRAG: Thinking to Retrieval Step by Step for Large Language Models
• DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments
• DeepResearchGym: A Free, Transparent, and Reproducible Evaluation Sandbox for Deep Research
• DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models
• Deflating Deflationism: A Critical Perspective on Debunking Arguments Against LLM Mentality
• DeLLMa: Decision Making Under Uncertainty with Large Language Models
• Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
• Demystifying Chains, Trees, and Graphs of Thoughts
• Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning
• Dense Retrieval Adaptation using Target Domain Description
• DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents
• Design Principles for Generative AI Applications
• Designing AI Personalities: Enhancing Human-Agent Interaction Through Thoughtful Persona Design
• Detecting Cognitive Distortions from Patient-Therapist Interactions
• Detecting Deception Using Natural Language Processing and Machine Learning in Datasets on COVID-19 and Climate Change
• Detecting hallucinations in large language models using semantic entropy
• Determinants of LLM-assisted Decision-Making
• Detoxify Language Model Step-by-Step
• Developing Effective Educational Chatbots with ChatGPT prompts: Insights from Preliminary Tests in a Case Study on Social Media Literacy
• Development and validation of large language model rating scales for automatically transcribed psychological therapy sessions
• Diagnosing Memorization in Chain-of-Thought Reasoning, One Token at a Time
• Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicine
• Dialog Inpainting: Turning Documents into Dialogs
• Dialoging Resonance: How Users Perceive, Reciprocate and React to Chatbot’s Self-Disclosure in Conversational Recommendations
• Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources
• Dialogue State Tracking with a Language Model using Schema-Driven Prompting
• Dialogue Transformers
• DialogueReason: Rule-Based RL Sparks Dialogue Reasoning in LLMs
• DiaSynth: Synthetic Dialogue Generation Framework for Low Resource Dialogue Applications
• Diffusion Language Models Know the Answer Before Decoding
• Diffusion LLMs Can Do Faster-Than-AR Inference via Discrete Diffusion Forcing
• Diffusion Models are Evolutionary Algorithms
• Diffusion-LM Improves Controllable Text Generation
• Diplomat: A Dialogue Dataset for Situated PragMATic Reasoning
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model
• Disambiguating Anthropomorphism and Anthropomimesis in Human-Robot Interaction
• Discourse Structure and Dialogue Acts in Multiparty Dialogue: the STAC Corpus
• Discourse-Level Representations can Improve Prediction of Degree of Anxiety
• Discovering Latent Concepts Learned in BERT
• Discursive Socratic Questioning: Evaluating the Faithfulness of Language Models’ Understanding of Discourse Relations
• DiscussLLM: Teaching Large Language Models When to Speak
• Dissociating language and thought in large language models
• Distilling LLMs' Decomposition Abilities into Compact Language Models
• Divide-or-Conquer? Which Part Should You Distill Your LLM?
• Do Cognitively Interpretable Reasoning Traces Improve LLM Performance?
• Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models
• Do Large Language Models Latently Perform Multi-Hop Reasoning?
• Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?
• Do Large Language Models Reason Causally Like Us? Even Better?
• Do large language models resemble humans in language use?
• Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcom
• Do LLMs Encode Functional Importance of Reasoning Tokens?
• Do LLMs Exhibit Human-Like Reasoning? Evaluating Theory of Mind in LLMs for Open-Ended Responses
• Do LLMs Possess a Personality? Making the MBTI Test an Amazing Evaluation for Large Language Models
• Do LLMs produce texts with "human-like" lexical diversity?
• Do LLMs Truly Understand When a Precedent Is Overruled?
• Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
• Do Models Really Learn to Follow Instructions? An Empirical Study of Instruction Tuning
• Do Prompt-Based Models Really Understand the Meaning of Their Prompts?
• Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection
• Do Role-Playing Agents Practice What They Preach? Belief-Behavior Consistency in LLM-Based Simulations of Human Trust
• Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?
• DO THEY SEE WHAT WE SEE?
• Do We Trust ChatGPT as much as Google Search and Wikipedia?
• DOC: Improving Long Story Coherence With Detailed Outline Control
• Does It Make Sense to Speak of Introspection in Large Language Models?
• Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
• Does Socialization Emerge in AI Agent Society? A Case Study of Moltbook
• Does Thinking More always Help? Understanding Test-Time Scaling in Reasoning Models
• Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search
• Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
• Domain-specific Question Answering with Hybrid Search
• Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary?
• Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration
• DPMT: Dual Process Multi-scale Theory of Mind Framework for Real-time Human-AI Collaboration
• DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research
• DR-HAI: Argumentation-based Dialectical Reconciliation in Human-AI Interactions
• DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models
• Drop the Hierarchy and Roles: How Self-Organizing LLM Agents Outperform Designed Structures
• DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought
• Durably reducing conspiracy beliefs through dialogues with AI
• Dynamic LLM-Agent Network: An LLM-agent Collaboration Framework with Agent Team Optimization
• Dynamic Planning with a LLM
• Dynamic Prompting: A Unified Framework for Prompt Tuning
• Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models
• Dynamic Task-Oriented Dialogue: A Comparative Study of Llama-2 and Bert in Slot Value Generation
• Dynamically Expandable Graph Convolution for Streaming Recommendation
• DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation
• Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining
• Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
• Educating LLMs like Human Students: Structure-aware Injection of Domain Knowledge
• Efficient Nearest Neighbor Language Models
• Efficient Reasoning with Balanced Thinking
• Efficient Reasoning with Hidden Thinking
• Efficient Reinforcement Learning via Large Language Model-based Search
• Efficient Streaming Language Models with Attention Sinks
• Efficient Tool Use with Chain-of-Abstraction Reasoning
• Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs
• Eliciting Latent Knowledge from Quirky Language Models
• Eliciting Reasoning in Language Models with Cognitive Tools
• Embarrassingly Shallow Autoencoders for Sparse Data*
• Embedding Domain Knowledge for Large Language Models via Reinforcement Learning from Augmented Generation
• Emergence of Abstractions: Concept Encoding and Decoding Mechanism for In-Context Learning in Transformers
• Emergent Hierarchical Reasoning In LLMs Through Reinforcement Learning
• Emergent Introspective Awareness in Large Language Models
• Emerging Properties in Unified Multimodal Pretraining
• EmotionPrompt: Leveraging Psychology for Large Language Models Enhancement via Emotional Stimulus
• Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems
• Empathy Through Multimodality in Conversational Interfaces
• Empirical Study of Symmetrical Reasoning in Conversational Chatbots
• Empowering Domain-Specific Language Models with Graph-Oriented Databases: A Paradigm Shift in Performance and Model Maintenance
• Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting
• Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph
• Enabling Self-Improving Agents to Learn at Test Time With Human-In-The-Loop Guidance
• Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
• End-to-End Test-Time Training for Long Context
• Energy-Based Transformers are Scalable Learners and Thinkers
• Enhancing AI-Assisted Group Decision Making through LLM-Powered Devil's Advocate
• Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation
• Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals
• Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision
• Enhancing Performance on Seen and Unseen Dialogue Scenarios using Retrieval-Augmented End-to-End Task-Oriented System
• Enhancing personalized multi-turn dialogue with curiosity reward
• Enhancing Pipeline-Based Conversational Agents with Large Language Model
• Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
• Enhancing social cohesion with cooperative bots in societies of greedy, mobile individuals
• Entangled in Representations: Mechanistic Investigation of Cultural Biases in Large Language Models
• Equipping agents for the real world with Agent Skills
• Escaping the Verifier: Learning to Reason via Demonstrations
• Estimating AI productivity gains from Claude conversations
• Evaluating Emotional Nuances In Dialogue Summarization
• Evaluating Large Language Models at Evaluating Instruction Following
• Evaluating Large Language Models in Exercises of UML Class Diagram Modeling
• Evaluating Large Language Models in Theory of Mind Tasks
• Evaluating the Efficacy of Interactive Language Therapy Based on LLM for High-Functioning Autistic Adolescent Psychological Counseling
• Evaluating the psychometric properties of ChatGPT-generated questions
• Evaluating the Therapeutic Alliance With a Free-Text CBT Conversational Agent (Wysa): A Mixed-Methods Study
• Evaluating Theory of Mind and Internal Beliefs in LLM-Based Multi-Agent Systems
• Evaluating Very Long-Term Conversational Memory of LLM Agents
• Evaluation and Benchmarking of LLM Agents: A Survey
• Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading
• Everything Everywhere All At Once: Llms Can In-context Learn Multiple Tasks In Superposition
• Evidence of Human-Level Bonds Established With a Digital Conversational Agent: Cross-sectional, Retrospective Observational Study
• EVINCE: Optimizing Multi-LLM Dialogues Using Conditional Statistics and Information Theory
• Evolving Deeper LLM Thinking
• Example 1:
• Example 2:
• Existential Conversations with Large Language Models: Content, Community, and Culture
• Expanding Explainability: Towards Social Transparency in AI systems
• Expedient Assistance and Consequential Misunderstanding: Envisioning an Operationalized Mutual Theory of Mind
• Explain-Query-Test: Self-Evaluating LLMs Via Explanation and Comprehension Discrepancy
• Explainable Compliance Detection with Multi-Hop Natural Language Inference on Assurance Case Structure
• Explainable Multimodal Emotion Reasoning
• Explainable Recommendation with Personalized Review Retrieval and Aspect Learning
• Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering
• Explicit Inductive Inference using Large Language Models
• Exploiting Dialogue Acts and Context to Identify Argumentative Relations in Online Debates
• Exploiting Explainability to Design Adversarial Attacks and Evaluate Attack Resilience in Hate-Speech Detection Models
• Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Tasks
• Exploring Format Consistency for Instruction Tuning
• Exploring Large Language Models for Knowledge Graph Completion
• Exploring LLMs Applications in Law: A Literature Review on Current Legal NLP Approaches
• Exploring Student-AI Interactions in Vibe Coding
• Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Review
• Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
• Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations
• Exploring the Potential of Large Language Models in Computational Argumentation
• Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers
• External Model Motivated Agents: Reinforcement Learning for Enhanced Environment Sampling
• Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
• Extracting memorized pieces of (copyrighted) books from open-weight language models
• Extrapolation by Association: Length Generalization Transfer in Transformers
• Extreme Multi-Label Skill Extraction Training using Large Language Models
• Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation
• Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model
• Faith and Fate: Limits of Transformers on Compositionality
• Faithful and Robust LLM-Driven Theorem Proving for NLI Explanations
• Fake News Detectors are Biased against Texts Generated by Large Language Models
• Fast and Slow Learning From Reviews
• Fast, Slow, and Tool-augmented Thinking for LLMs: A Review
• Federation of Agents: A Semantics-Aware Communication Fabric for Large-Scale Agentic AI
• FinCoT: Grounding Chain-of-Thought in Expert Financial Reasoning
• Find the Gap: AI, Responsible Agency and Vulnerability
• Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences
• Fine-grained Hallucination Detection and Editing for Language Models
• Fine-tuning Language Models for Factuality
• Fine-tuning Large Language Model for Automated Algorithm Design
• Fine-tuning Pre-trained Language Models for Dialogical Argument Mining with Inference Anchoring Theory
• First Try Matters: Revisiting the Role of Reflection in Reasoning Models
• FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets
• Flattery, Fluff, and Fog: Diagnosing and Mitigating Idiosyncratic Biases in Preference Models
• Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities
• FlowReasoner: Reinforcing Query-Level Meta-Agents
• Flows: Building Blocks of Reasoning and Collaborating AI
• Forecasting the presence and intensity of hostility on Instagram using linguistic and social features
• Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning
• FormulaOne: Measuring the Depth of Algorithmic Reasoning Beyond Competitive Programming
• Foundation Priors
• Foundations of Large Language Models
• From Articles to Code: On-Demand Generation of Core Algorithms from Scientific Publications
• From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models
• From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step
• From Five Dimensions to Many: Large Language Models as Precise and Interpretable Psychological Profilers
• From Human to Machine Psychology: A Conceptual Framework for Understanding Well-Being in Large Language Models
• From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization
• From Language to Logic: A Bi-Level Framework for Structured Reasoning
• From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models
• From Local to Global: A Graph RAG Approach to Query-Focused Summarization
• From Louvain to Leiden: guaranteeing well-connected communities
• From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?
• From Persona to Person: Enhancing the Naturalness with Multiple Discourse Relations Graph Learning in Personalized Dialogue Generation
• From Prompt Engineering to Prompt Science With Human in the Loop
• From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
• From speaking like a person to being personal: The effects of personalized, regular interactions with conversational agents
• From Text to Emoji: How PEFT-Driven Personality Manipulation Unleashes the Emoji Potential in LLMs
• From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning
• From Trial-and-Error to Improvement: A Systematic Analysis of LLM Exploration Mechanisms in RLVR
• From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
• Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report
• Further Explorations on the Use of Large Language Models for Thematic Analysis. Open-Ended Prompts, Better Terminologies and Thematic Maps
• Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce
• Gdpval: Evaluating Ai Model Performance On Real-world Economically Valuable Tasks
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• GenAI as a Power Persuader: How Professionals Get Persuasion Bombed When They Attempt to Validate LLMs
• General
• Generalization through Memorization: Nearest Neighbor Language Models
• Generalization to New Sequential Decision Making Tasks with In-Context Learning
• Generating Proto-Personas through Prompt Engineering: A Case Study on Efficiency, Effectiveness and Empathy
• Generating Query-Relevant Document Summaries via Reinforcement Learning
• Generative Agent Simulations of 1,000 People
• Generative Agents: Interactive Simulacra of Human Behavior
• Generative AI in Real-World Workplaces
• Generative Interfaces for Language Models
• Generative Models as a Complex Systems Science: How can we make sense of large language model behavior?
• Generator-Retriever-Generator: A Novel Approach to Open-domain Question Answering
• GenPRM: Scaling Test-Time Compute of Process Reward Models via Generative Reasoning
• GenRec: Large Language Model for Generative Recommendation
• GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency
• GHPO: Adaptive Guidance for Stable and Efficient LLM Reinforcement Learning
• GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation
• Goal Alignment in LLM-Based User Simulators for Conversational AI
• Goals, Plans, and Action Models
• Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
• GPT-4 as a Homework Tutor can Improve Student Engagement and Learning Outcomes
• GPT-4 is judged more human than humans in displaced and inverted Turing tests
• Gradual Disempowerment: Systemic Existential Risks from Incremental AI Development
• Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks
• Graph of Thoughts: Solving Elaborate Problems with Large Language Models
• Graph-enhanced Large Language Models in Asynchronous Plan Reasoning
• GRASP: Municipal Budget AI Chatbots for Enhancing Civic Engagement
• Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization
• Grounding Gaps in Language Model Generations
• Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning
• Grounding Multilingual Multimodal LLMs With Cultural Knowledge
• Grounding ‘Grounding’ in NLP
• Guidance is All You Need: Temperature-Guided Reasoning in Large Language Models
• Guiding Large Language Models via Directional Stimulus Prompting
• H2HTalk: Evaluating Large Language Models as Emotional Companion
• Hallucinating with AI: AI Psychosis as Distributed Delusions
• Hallucination is Inevitable: An Innate Limitation of Large Language Models
• Harnessing Business and Media Insights with Large Language Models
• Has the Creativity of Large-Language Models peaked? —an analysis of inter- and intra-LLM variability —
• Hello Again! LLM-powered Personalized Agent for Long-term Dialogue
• Hierarchical Reasoning Model
• HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches
• HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
• HiTKG: Towards Goal-Oriented Conversations via Multi-Hierarchy Learning
• Hogwild! Inference: Parallel LLM Generation via Concurrent Attention
• Holy Grail 2.0: From Natural Language to Constraint Models
• HonestBait: Forward References for Attractive but Faithful Headline Generation
• Hop, Skip, and Overthink: Diagnosing Why Reasoning Models Fumble during Multi-Hop Analysis
• How AI Impacts Skill Formation
• How do Transformers Learn Implicit Reasoning?
• How Exposed Are UK Jobs to Generative AI? Developing and Applying a Novel Task-Based Index
• How Far Are We from Genuinely Useful Deep Research Agents?
• How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs
• How Many Instructions Can LLMs Follow at Once?
• How much do language models memorize?
• How Multimodal LLMs Solve Image Tasks: A Lens on Visual Grounding, Task Reasoning, and Answer Decoding
• How new data permeates LLM knowledge and how to dilute it
• How Should We Meta-Learn Reinforcement Learning Algorithms?
• How susceptible are LLMs to Logical Fallacies?
• How to Correctly do Semantic Backpropagation on Language-based Agentic Systems
• How we built our multi-agent research system
• How well can large language models explain business processes?
• HowProjective is Projective Content? Gradience in Projectivity and At-issueness
• HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs
• Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks
• Humans learn to prefer trustworthy AI over human partners
• Humans or LLMs as the Judge? A Study on Judgement Biases
• Humans overrely on overconfident language models, across languages
• Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning
• Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing
• Hyperagents
• HyperBandit: Contextual Bandit with Hypernetwork for Time-Varying User Preferences in Streaming Recommendation
• Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models
• I like it... I like it not: Evaluating User Ratings Noise in Recommender Systems
• Identification of Propositional and Illocutionary Relations
• IFEvalCode: Controlled Code Generation
• IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction
• Implicit Chain of Thought Reasoning via Knowledge Distillation
• Improving Chain-of-Thought Reasoning via Quasi-Symbolic Abstractions
• Improving Conversational Recommender Systems via Transformer-based Sequential Modelling
• Improving Dialog Systems for Negotiation with Personality Modeling
• Improving Document-Level Sentiment Analysis with User and Product Context
• Improving Factuality and Reasoning in Language Models through Multiagent Debate
• Improving Generalization in Task-oriented Dialogues with Workflows and Action Plans
• Improving large language models with concept-aware fine-tuning
• Improving Reinforcement Learning from Human Feedback Using Contrastive Rewards
• Improving Small-Scale Large Language Models Function Calling for Reasoning Tasks
• In-context learning agents are asymmetric belief updaters
• Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems
• Inducing Positive Perspectives with Text Reframing
• Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs
• Inference-Aware Prompt Optimization for Aligning Black-Box Large Language Models
• Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
• Inference-Time Scaling for Generalist Reward Modeling
• Information-Theoretic Reward Decomposition for Generalizable RLHF
• Informed Named Entity Recognition Decoding For Generative Language Models
• Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey
• InMind: Evaluating LLMs in Capturing and Applying Individual Human Reasoning Styles
• Insert-expansions For Tool-enabled Conversational Agents
• Inspecting and Editing Knowledge Representations in Language Models
• INSPIRED: Toward Sociable Recommendation Dialog Systems
• Instance-adaptive Zero-shot Chain-of-Thought Prompting
• Instruction
• Instruction Induction: From Few Examples to Natural Language Task Descriptions
• Instruction Tuning for Large Language Models: A Survey
• Integrating Large Language Models and Reinforcement Learning for Non-Linear Reasoning
• IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI Systems
• Intelligent AI Delegation
• Intent Mismatch Causes LLMs to Get Lost in Multi-Turn Conversation
• Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues
• Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy
• Interaction Dynamics as a Reward Signal for LLMs
• Interactions with generative AI chatbots: unveiling dialogic dynamics, students’ perceptions, and practical competencies in creative problem-solving
• Interesting Scientific Idea Generation Using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders
• Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation
• Interpretation modeling: Social grounding of sentences by reasoning over their implicit moral judgments
• Interrogator
• Intrinsically Motivated Graph Exploration Using Network Theories of Human Curiosity
• InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models
• Invalid Logic, Equivalent Gains: The Bizarreness of Reasoning in Language Model Prompting
• Inverse-Q*: Token Level Reinforcement Learning for Aligning Large Language Models Without Preference Data
• Investigating Gender Bias in Language Models Using Causal Mediation Analysis
• Investigating task-specific prompts and sparse autoencoders for activation monitoring
• Irony in Emojis: A Comparative Study of Human and LLM Interpretation
• Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens
• Is Sarcasm Detection A Step-by-Step Reasoning Process in Large Language Models?
• Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs
• It’s All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization
• J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement Learning
• Jamba: A Hybrid Transformer-Mamba Language Model
• JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering
• Jointly Reinforcing Diversity and Quality in Language Model Generations
• Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
• KellyBench: Can Language Models Beat the Market?
• KETOD: Knowledge-Enriched Task-Oriented Dialogue
• KGAT: Knowledge Graph Attention Network for Recommendation
• KiPT: Knowledge-injected Prompt Tuning for Event Detection
• Klear-Reasoner: Advancing Reasoning Capability via Gradient-Preserving Clipping Policy Optimization
• Knowledge Distillation for Enhancing Walmart E-commerce Search Relevance Using Large Language Models
• Knowledge Graph Prompting for Multi-Document Question Answering
• Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains
• Knowledge Retrieval Based on Generative AI
• Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations
• KoLA: Carefully Benchmarking World Knowledge of Large Language Models
• KTO: Model Alignment as Prospect Theoretic Optimization
• Language Agents as Optimizable Graphs
• Language as a Cognitive Tool to Imagine Goals in Curiosity-Driven Exploration
• Language Model Personalization via Reward Factorization
• Language Modeling by Language Models
• Language Modeling is Compression
• Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought
• Language Models are Pragmatic Speakers
• Language models are weak learners
• Language Models Learn to Mislead Humans via RLHF
• Language models show human-like content effects on reasoning tasks
• Language Models’ Hall of Mirrors Problem: Why AI Alignment Requires Peircean Semiosis
• Large Action Models: From Inception to Implementation
• Large Causal Models From Large Language Models
• Large Concept Models: Language Modeling in a Sentence Representation Space
• Large Language Diffusion Models
• Large Language Model Agents Are Not Always Faithful Self-Evolvers
• Large Language Model based Multi-Agents: A Survey of Progress and Challenges
• Large Language Model Guided Tree-of-Thought
• Large Language Model Programs
• Large Language Model Reasoning Failures
• Large Language Model-based Data Science Agent: A Survey
• Large Language Model-Brained GUI Agents: A Survey
• Large Language Models and Knowledge Graphs: Opportunities and Challenges
• Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments
• Large Language Models Are Human-level Prompt Engineers
• Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners
• Large Language Models are Zero-Shot Rankers for Recommender Systems
• Large Language Models as Planning Domain Generators
• Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?*
• Large Language Models as Zero-Shot Conversational Recommenders
• Large Language Models can accomplish Business Process Management Tasks
• Large Language Models Can Infer Psychological Dispositions of Social Media Users
• Large language models can segment narrative events similarly to humans
• Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation
• Large Language Models Do Not Simulate Human Psychology
• Large Language Models For Social Networks: Applications, Challenges, And Solutions
• Large Language Models for User Interest Journeys
• Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search
• Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
• Large Language Models Reflect the Ideology of their Creators
• Large Language Models Report Subjective Experience Under Self-Referential Processing
• Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions
• Large language models surpass human experts in predicting neuroscience results
• Large Language Models Think Too Fast To Explore Effectively
• Large Linguistic Models: Investigating LLMs' metalinguistic abilities
• Large Models of What? Mistaking Engineering Achievements for Human Linguistic Agency
• Large Multimodal Agents: A Survey
• Large Scale Product Graph Construction for Recommendation in E-commerce
• Latent Collaboration in Multi-Agent Systems
• Latent Skill Discovery for Chain-of-Thought Reasoning
• LatentQA: Teaching LLMs to Decode Activations Into Natural Language
• Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers
• Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments
• Learning "Partner-Aware" Collaborators in Multi-Party Collaboration
• Learning Distributed Representations from Reviews for Collaborative Filtering
• Learning Human-Object Interaction as Groups
• Learning Pluralistic User Preferences through Reinforcement Learning Fine-tuned Summaries
• Learning Retrieval Augmentation for Personalized Dialogue Generation
• Learning to (Learn at Test Time): RNNs with Expressive Hidden States
• Learning to Ask Appropriate Questions in Conversational Recommendation
• Learning to Ask Critical Questions for Assisting Product Search
• Learning to Discover at Test Time
• Learning To Guide Human Experts Via Personalized Large Language Models
• Learning to Map Context-Dependent Sentences to Executable Formal Queries
• Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge
• Learning to Rank for Recommender Systems
• Learning to Reason for Factuality
• Learning to Reason without External Rewards
• Learning to Relate to Previous Turns in Conversational Search
• Learning To Retrieve Prompts for In-Context Learning
• Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering
• Learning to Select the Relevant History Turns in Conversational Question Answering
• Learning to Think: Information-Theoretic Reinforcement Fine-Tuning for LLMs
• Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders
• Least-to-most Prompting Enables Complex Reasoning In Large Language Models
• LESS: Selecting Influential Data for Targeted Instruction Tuning
• Lessons Learnt From Consolidating ML Models in a Large Scale Recommendation System
• Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models
• Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short Ones
• Let’s Verify Step by Step
• Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity
• Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generation
• Leveraging Large Language Models in Conversational Recommender Systems
• Leveraging LLMs for KPIs Retrieval from Hybrid Long-Document: A Comprehensive Framework and Dataset
• Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning
• Lexical Entrainment for Conversational Systems
• Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models
• Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways
• LIMA: Less Is More for Alignment
• LIMI: Less is More for Agency
• LIMOPro: Reasoning Refinement for Efficient and Effective Test-time Scaling
• Linguistic Alignment in Conversational AI: A Systematic Review of Cognitive-Linguistic Dimensions, Measurements, and User Outcomes (2020–2025)
• Linguistic Blind Spots of Large Language Models
• Linguistic markers of inherently false AI communication and intentionally false human communication: Evidence from hotel reviews
• LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries
• LLaMA-Omni: Seamless Speech Interaction with Large Language Models
• LLM Augmentations to support Analytical Reasoning over Multiple Documents
• LLM Generated Persona is a Promise with a Catch
• LLM Post-Training: A Deep Dive into Reasoning Large Language Models
• LLM Reasoning Is Latent, Not the Chain of Thought
• LLM Strategic Reasoning: Agentic Study through Behavioral Game Theory
• LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
• LLM-based Conversational AI Therapist for Daily Functioning Screening and Psychotherapeutic Intervention via Everyday Smart Devices
• LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback
• LLM-Rec: Personalized Recommendation via Prompting Large Language Models
• LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
• LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization
• LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools
• LLMs are Frequency Pattern Learners in Natural Language Inference
• LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities
• LLMs as Architects and Critics for Multi-Source Opinion Summarization
• LLMs as Method Actors: A Model for Prompt Engineering and Architecture
• LLMs can be Fooled into Labelling a Document as Relevant
• LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring
• LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!
• LLMs can implicitly learn from mistakes in-context
• LLMs Get Lost In Multi-Turn Conversation
• LLMs Struggle to Reject False Presuppositions when Misinformation Stakes are High
• Local Coherence or Global Validity? Investigating RLVR Traces in Math Domains
• Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning
• Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models
• Logical Reasoning in Large Language Models: A Survey
• LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models
• Long-context LLMs Struggle with Long In-context Learning
• Long-form Factuality In Large Language models
• Longer Context, Deeper Thinking: Uncovering the Role of Long-Context Ability in Reasoning
• LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering
• LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs
• Looking beyond the next token
• Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers
• Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models
• Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs
• LR^2Bench: Evaluating Long-chain Reflective Reasoning Capabilities of Large Language Models via Constraint Satisfaction Problems
• LSR: Reinforcement Learning with Supervised Reward Outperforms SFT in Instruction Following
• Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models
• Machine ex machina: A Framework Decentering  the Human in AI Design Praxis
• Machine gaze in online behavioral targeting: The effects of algorithmic human likeness on social presence and social influence
• Machine Psychology
• Magentic-UI: Towards Human-in-the-loop Agentic Systems
• Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
• Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning
• Making Sense of Memory in AI Agents
• Man vs machine – Detecting deception in online reviews
• MAPS: A Multi-Agent Framework Based on Big Seven Personality and Socratic Guidance for Multimodal Scientific Problem Solving
• MARS: A Multi-Agent Framework Incorporating Socratic Guidance for Automated Prompt Optimization
• MasRouter: Learning to Route LLMs for Multi-Agent Systems
• Mastering Diverse Domains through World Models
• MatFormer: Nested Transformer for Elastic Inference
• Mathematical methods and human thought in the age of AI
• MCP-Zero: Proactive Toolchain Construction for LLM Agents from Scratch
• Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs
• Meanings are like Onions: a Layered Approach to Metaphor Processing
• Measuring Agents in Production
• Measuring Alliance and Symptom Severity in Psychotherapy Transcripts Using Bert Topic Modeling
• Measuring and Mitigating Persona Distortions from AI Writing Assistance
• Measuring Faithfulness in Chain-of-Thought Reasoning
• Measuring Human Preferences in RLHF is a Social Science Problem
• Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models
• Measuring the Value of Social Dynamics in Online Product Ratings Forums
• Mechanisms of Introspective Awareness
• Mechanistic Indicators of Understanding in Large Language Models
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications
• Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads
• MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation
• Memorization and Knowledge Injection in Gated LLMs
• Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models
• Memory Sandbox: Transparent and Interactive Memory Management for Conversational Agents
• Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models
• Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge
• Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving
• Metacognitive Prompting Improves Understanding in Large Language Models
• Metacognitive Retrieval-Augmented Large Language Models
• Metacognitive Reuse: Turning Recurring LLM Reasoning Into Concise Behaviors
• Metadiscursive nouns in academic argument: ChatGPT vs student practices
• Metagpt: Meta Programming For Multi-agent Collaborative Framework
• MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems
• Methodologies for Improving Modern Industrial Recommender Systems
• Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models
• Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
• Minds versus Machines: Rethinking Entailment Verification with Language Models
• MindSearch: Mimicking Human Minds Elicits Deep AI Searcher
• Mindstorms in Natural Language-Based Societies of Mind
• MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
• Mining Hidden Thoughts from Texts: Evaluating Continual Pretraining with Synthetic Data for LLM Reasoning
• Misaligned by Design: Incentive Failures in Machine Learning
• Missing Premise exacerbates Overthinking: Are Reasoning Models losing Critical Thinking Skill?
• Mitigating Hallucinations in Large Language Models via Causal Reasoning
• Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just What They Say
• Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for Large Language Models
• MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement
• MLLM-CBench: A Comprehensive Benchmark for Continual Instruction Tuning of Multimodal LLMs with Chain-of-Thought Reasoning Analysis
• MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
• Model Organisms for Emergent Misalignment
• Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence
• Modeling Appropriate Language in Argumentation
• Modeling Code: Is Text All You Need?
• Modeling Interpersonal Linguistic Coordination in Conversations using Word Mover's Distance
• Modeling the Quality of Dialogical Explanations
• MODS: Moderating a Mixture of Document Speakers to Summarize Debatable Queries in Document Collections
• MOMENTS: A Comprehensive Multimodal Benchmark for Theory of Mind
• Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation
• Monolith: Real Time Recommendation System With Collisionless Embedding Table
• MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis
• Mostly Exploration-Free Algorithms for Contextual Bandits
• Multi-Agent Collaborative Intelligence: Dual-Dial Control for Reliable LLM Reasoning
• Multi-agent cooperation through in-context co-player inference
• Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation
• Multi-hop Question Answering via Reasoning Chains
• Multi-Task End-to-End Training Improves Conversational Recommendation
• Multi-Token Attention
• Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains
• MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMs
• Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention
• Natural Emergent Misalignment From Reward Hacking In Production RL
• Natural Emergent Misalignment From Reward Hacking In Production Rl
• Navigating the State of Cognitive Flow: Context-Aware AI Interventions for Effective Reasoning Support
• Nested Attention: Semantic-aware Attention Values for Concept Personalization
• Nested Learning: The Illusion of Deep Learning Architecture Expanded
• Nested Learning: The Illusion of Deep Learning Architectures
• Neural Approaches to Conversational AI
• Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning
• Neural Collaborative Filtering
• Neural Collaborative Filtering vs. Matrix Factorization Revisited
• Neural Conversation Models and How to Rein Them in: A Survey of Failures and Fixes
• Neural Topic Modeling of Psychotherapy Sessions
• Neuro-Symbolic AI in 2024: A Systematic Review
• NeuroQL: A Neuro-Symbolic Language and Dataset for Inter-Subjective Reasoning
• Neurosymbolic AI- Why, What, and How
• Neutralizing Bias in LLM Reasoning using Entailment Graphs
• News Sentiment Embeddings for Stock Price Forecasting
• News Source Citing Patterns in AI Search Systems
• Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
• Next Steps for Human-Centered Generative AI: A Technical Perspective
• No that's not what I meant: Handling Third Position Repair in Conversational Question Answering
• Not All Parameters Are Created Equal: Smart Isolation Boosts Fine-Tuning Performance
• NoveltyBench: Evaluating Language Models for Humanlike Diversity
• Octopus v2: On-device language model for super agent
• Octopus v4: Graph of language models
• Off-Policy Evaluation for Large Action Spaces via Policy Convolution
• OMNI-SIMPLEMEM: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory
• Omni-Thinker: Scaling Multi-Task RL in LLMs with Hybrid Reward and Task Scheduling
• OmniParser for Pure Vision Based GUI Agent
• OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking
• On Generative Agents in Recommendation
• On Information Distortions in Online Ratings
• On Information Self-Locking in Reinforcement Learning for Active Reasoning of LLM agents
• On the Adaptive Psychological Persuasion of Large Language Models
• On the Binding Problem in Artificial Neural Networks
• On the Conversational Basis of Some Presuppositions
• On the Impact of Fine-Tuning on Chain-of-Thought Reasoning
• On the Limits of Innate Planning in Large Language Models
• On The Persona-based Summarization of Domain-Specific Documents
• On the Reasoning Capacity of AI Models and How to Quantify It
• On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models
• On the Roles of LLMs in Planning: Embedding LLMs into Planning Graphs
• On the Theoretical Limitations of Embedding-Based Retrieval
• On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting
• Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback
• Open Models, Closed Minds? On Agents Capabilities in Mimicking Human Personalities through Open Large Language Models
• Open Problems in Mechanistic Interpretability
• Openagents: An Open Platform For Language Agents In The Wild
• OpenAssistant Conversations - Democratizing Large Language Model Alignment
• OpenClaw-RL: Train Any Agent Simply by Talking
• OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs
• OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
• OpenThoughts: Data Recipes for Reasoning Models
• Operating Multi-Client Influence Networks Across Platforms
• OpinionConv: Conversational Product Search with Grounded Opinions
• Opportunities for large language models and discourse in engineering design
• OptimalThinkingBench: Evaluating Over and Underthinking in LLMs
• Orchestrating Synthetic Data with Reasoning
• Outcome-based Exploration for LLM Reasoning
• Overconfidence in LLM-as-a-Judge: Diagnosis and Confidence-Driven Solution
• Overview of DialAM-2024: Argument Mining in Natural Language Dialogues
• PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing
• Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning
• PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals
• Peer-Preservation in Frontier Models
• PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods
• People cannot distinguish GPT-4 from a human in a Turing test
• Performative Thinking? The Brittle Correlation Between CoT Length and Problem Complexity
• Persistent Pre-Training Poisoning of LLMs
• PersLLM: A Personified Training Approach for Large Language Models
• Persona Generators: Generating Diverse Synthetic Personas at Scale
• Persona Vectors: Monitoring and Controlling Character Traits in Language Models
• Persona-Assigned Large Language Models Exhibit Human-Like Motivated Reasoning
• PersonaAgent: When Large Language Model Agents Meet Personalization at Test Time
• PersonaGym: Evaluating Persona Agents and LLMs
• Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback
• Personalization of Large Language Models: A Survey
• Personalized Dialogue Generation with Persona-Adaptive Attention
• Personalized Language Modeling from Personalized Human Feedback
• Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning
• PersonaPKT: Building Personalized Dialogue Agents via Parameter-efficient Knowledge Transfer
• Persuasive presuppositions
• PersuasiveToM: A Benchmark for Evaluating Machine Theory of Mind in Persuasive Dialogues
• Perturbation CheckLists for Evaluating NLG Evaluation Metrics
• Pixel-Level Reasoning Segmentation via Multi-turn Conversations
• Pixels, Patterns, but No Poetry: To See The World like Humans
• Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts
• Planning in Strawberry Fields: Evaluating and Improving the Planning and Scheduling Capabilities of LRM o1
• Planning Like Human: A Dual-process Framework for Dialogue Planning
• PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers
• Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs
• Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents
• Polanyi’s Revenge and AI’s New Romance with Tacit Knowledge
• PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking
• POMDP-based Statistical Spoken Dialogue Systems: a Review
• Position: LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks
• Position: Towards Bidirectional Human-AI Alignment
• Post-Completion Learning for Language Models
• Post-training for Efficient Communication via Convention Formation
• Post-Training Large Language Models via Reinforcement Learning from Self-Feedback
• PosterMate: Audience-driven Collaborative Persona Agents for Poster Design
• Posting versus Lurking: Communicating in a Multiple Audience Context
• Potemkin Understanding in Large Language Models
• Pragmatic Implicature Processing in ChatGPT
• Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
• Pre-Trained Policy Discriminators are General Reward Models
• Precise Zero-Shot Dense Retrieval without Relevance Labels
• Predictive Preference Learning from Human Interventions
• Preference Discerning with LLM-Enhanced Generative Retrieval
• Prefix-Tuning: Optimizing Continuous Prompts for Generation
• PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts
• Premise Order Matters in Reasoning with Large Language Models
• Premise-Augmented Reasoning Chains Improve Error Identification in Math reasoning with LLMs
• Presuppositions are more persuasive than assertions if addressees accommodate them: Experimental evidence for philosophical reasoning
• Pretrained Language Models as Containers of the Discursive Knowledge
• PRewrite: Prompt Rewriting with Reinforcement Learning
• PRIME: Large Language Model Personalization with Cognitive Memory and Thought Processes
• Pro-Active Systems and Influenceable Users: Simulating Pro-Activity in Task-oriented Dialogues
• Proactive behavior in voice assistants: A systematic review and conceptual model
• Proactive Conversational Agents in the Post-ChatGPT World
• Proactive Conversational Agents with Inner Thoughts
• Proactive Human-Machine Conversation with Explicit Conversation Goals
• Proactive Moderation of Online Discussions: Existing Practices and the Potential for Algorithmic Support
• ProAgent: Building Proactive Cooperative Agents with Large Language Models
• Probing Structured Semantics Understanding and Generation of Language Models via Question Answering
• Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games
• Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words
• Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models
• Process Reward Models That Think
• Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks
• Progress Measures For Grokking Via Mechanistic Interpretability
• Progressive-Hint Prompting Improves Reasoning in Large Language Models
• Prompt Architecture Determines Reasoning Quality: A Variable Isolation Study on the Car Wash Problem
• Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
• Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
• Prompted LLMs as Chatbot Modules for Long Open-domain Conversation
• Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration
• Prompting Large Language Models for Recommender Systems: A Comprehensive Framework and Empirical Analysis
• Prompting Large Language Models With the Socratic Method
• Pron vs Prompt: Can Large Language Models already Challenge a World-Class Fiction Author at Creative Text Writing?
• Propositional Interpretability in Artificial Intelligence
• ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models
• ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs
• ProsocialDialog: A Prosocial Backbone for Conversational Agents
• ProtoReasoning: Prototypes as the Foundation for Generalizable Reasoning in LLMs
• Proxona: Leveraging LLM-Driven Personas to Enhance Creators' Understanding of Their Audience
• PsychAdapter: Adapting LLM Transformers to Reflect Traits, Personality and Mental Health
• Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning
• Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations With a Chatbot
• Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations With a Chatbot
• Psychologically Enhanced AI Agents
• Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamics
• PsyDT: Using LLMs to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling
• Pushdown Layers: Encoding Recursive Structure in Transformer Language Models
• Pushing the Limits of Rule Reasoning in Transformers through Natural Language Satisfiability
• QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration
• Quantifying Controversy on Social Media
• Quantifying Human-AI Synergy
• Quantitative Introspection in Language Models: Tracking Internal States Across Conversation
• Query Rewriting for Retrieval-Augmented Large Language Models
• Query Understanding in the Age of Large Language Models
• QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?
• Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis
• Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
• R-Zero: Self-Evolving Reasoning LLM from Zero Data
• R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
• RAG Does Not Work for Enterprises
• RAG-Gym: Systematic Optimization of Language Agents for Retrieval-Augmented Generation
• RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism
• Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains
• RARR: Researching and Revising What Language Models Say, Using Language Models
• Re3: Generating Longer Stories With Recursive Reprompting and Revision
• React - Synergizing Reasoning And Acting In Language Models
• Real-time News Story Identification
• Real-Time Procedural Learning From Experience for AI Agents
• Real-World Planning with PDDL+ and Beyond
• ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought Reasoning in LLMs
• Reasoning Beyond Chain-of-Thought: A Latent Computational Mode in Large Language Models
• Reasoning Can Hurt the Inductive Abilities of Large Language Models
• Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference
• Reasoning Language Models: A Blueprint
• Reasoning LLMs are Wandering Solution Explorers
• Reasoning Models Are More Easily Gaslighted Than You Think
• Reasoning Models Can Be Effective Without Thinking
• Reasoning Models Don't Always Say What They Think
• Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination
• Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks
• Reasoning Strategies in Large Language Models: Can They Follow, Prefer, and Optimize?
• Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought
• Reasoning to Learn from Latent Thoughts
• Reasoning with Large Language Models, a Survey
• ReasonVQA: A Multi-hop Reasoning Benchmark with Structural Knowledge for Visual Question Answering
• Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations
• RecExplainer: Aligning Large Language Models for Recommendation Model Interpretability
• Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
• Recommendation systems and convergence of online reviews: The type of product network matters!
• Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
• Recommender Systems with Social Regularization
• Recommending What Video to Watch Next: A Multitask Ranking System
• ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs
• Reconciling the accuracy-diversity trade-off in recommendations
• Recursive Introspection: Teaching Language Model Agents How to Self-Improve
• Recursive Language Models
• Reflect then Learn: Active Prompting for Information Extraction Guided by Introspective Confusion
• Reflexion: an autonomous agent with dynamic memory and self-reflection
• Reinforced Language Models for Sequential Decision Making
• Reinforcement Learning be Enough for Thinking?
• Reinforcement Learning Finetunes Small Subnetworks in Large Language Models
• Reinforcement Learning for Optimizing RAG for Domain Chatbots
• Reinforcement Learning for Reasoning in Large Language Models with One Training Example
• Reinforcement Learning with Rubric Anchors
• Reinforcement Pre-Training
• Reinforcing General Reasoning without Verifiers
• RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns
• Representation biases: will we achieve complete understanding by analyzing representations?
• Representation Engineering: A Top-Down Approach to AI Transparency
• Reranking-based Generation for Unbiased Perspective Summarization
• ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
• Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agents
• Rethinking Conversational Agents in the Era of LLMs: Proactivity, Non-collaborativity, and Beyond
• Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning
• Rethinking Large Language Models in Mental Health Applications
• Rethinking STS and NLI in Large Language Models
• Rethinking Thinking Tokens: LLMs as Improvement Operators
• Rethinking with Retrieval: Faithful Large Language Model Inference
• Retrieval Head Mechanistically Explains Long-Context Factuality
• Retrieval-augmented reasoning with lean language models
• RevCore: Review-augmented Conversational Recommendation
• Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up
• Reverse Thinking Makes LLMs Stronger Reasoners
• Review-LLM: Harnessing Large Language Models for Personalized Review Generation
• Revisiting LLM Reasoning via Information Bottleneck
• Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation
• Revisiting RAG Ensemble: A Theoretical and Mechanistic Analysis of Multi-RAG System Collaboration
• Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?
• Revolutionizing Mental Health Support: An Innovative Affective Mobile Framework for Dynamic, Proactive, and Context-Adaptive Conversational Agents
• Reward Reasoning Model
• Reward-Robust RLHF in LLMs
• RewardBench: Evaluating Reward Models for Language Modeling
• Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment
• ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models
• Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing
• Rhetorical XAI: Explaining AI’s Benefits as well as its Use via Rhetorical Design
• RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation
• Rise of Machine Agency: A Framework for Studying the Psychology of Human–AI Interaction (HAII)
• RL + Transformer = A General-Purpose Problem Solver
• RL Squeezes, SFT Expands: A Comparative Study of Reasoning LLMs
• RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization
• RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner
• RLAD: Training LLMs to Discover Abstractions for Solving Reasoning Problems
• RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
• RLHF Workflow: From Reward Modeling to Online RLHF
• RLNVR: Reinforcement Learning from Non-Verified Real-World Rewards
• RLP: Reinforcement as a Pretraining Objective
• RLPR: Extrapolating RLVR to General Domains without Verifiers
• RLVER: Reinforcement Learning with Verifiable Emotion Rewards for Empathetic Agents
• RLVMR: Reinforcement Learning with Verifiable Meta-Reasoning Rewards for Robust Long-Horizon Agents
• RM-R1: Reward Modeling as Reasoning
• Role play with large language models
• Role-Play with Large Language Models
• RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
• RouteLLM: Learning to Route LLMs with Preference Data
• rStar2-Agent: Agentic Reasoning Technical Report
• Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains
• Rule2Text: Natural Language Explanation of Logical Rules in Knowledge Graphs
• S1-Bench: A Simple Benchmark for Evaluating System 1 Thinking Capability of Large Reasoning Models
• SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval
• Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
• Sample More to Think Less: Group Filtered Policy Optimization for Concise Reasoning
• SAND: Boosting LLM Agents with Self-Taught Action Deliberation
• Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search
• Scalable Language Models with Posterior Inference of Latent Thought Vectors
• Scalable Neural Contextual Bandit for Recommender Systems
• Scaling can lead to compositional generalization
• Scaling Expert Language Models with Unsupervised Domain Discovery
• Scaling Laws for Neural Language Models
• Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs
• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
• Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models
• Scaling Synthetic Data Creation with 1,000,000,000 Personas
• Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
• Schema-learning and rebinding as mechanisms of in-context learning and emergence
• SciTopic: Enhancing Topic Discovery in Scientific Literature through Advanced LLM
• SDPO: Segment-Level Direct Preference Optimization for Social Agents
• SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs
• Search Arena: Analyzing Search-Augmented LLMs
• Search-o1: Agentic Search-Enhanced Large Reasoning Models
• Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
• Searching for Best Practices in Retrieval-Augmented Generation
• See you soon again, chatbot? A design taxonomy to characterize user-chatbot relationships with different time horizons
• Seeing, Listening, Remembering, and Reasoning: A Multimodal Agent with Long-Term Memory
• Seemingly Conscious AI Risks
• Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning
• Self Selection and Information Role of Online Product Reviews
• Self-Adapting Language Models
• Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems
• Self-Alignment with Instruction Backtranslation
• Self-consistency Improves Chain Of Thought Reasoning In Language Models
• Self-critiquing models for assisting human evaluators
• Self-Directed Synthetic Dialogues and Revisions Technical Report
• Self-Discover: Large Language Models Self-Compose Reasoning Structures
• Self-distillation Enables Continual Learning
• Self-Evaluation Guided Beam Search for Reasoning
• Self-Improving Model Steering
• Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges
• SELF-INSTRUCT: Aligning Language Models with Self-Generated Instructions
• Self-Organizing Graph Reasoning Evolves into a Critical State for Continuous Discovery Through Structural-Semantic Dynamics
• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
• Self-Questioning Language Models
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
• Self-Reasoning Language Models: Unfold Hidden Reasoning Chains with Few Reasoning Catalyst
• Self-Refine: Iterative Refinement with Self-Feedback
• Self-reflecting Large Language Models: A Hegelian Dialectical Approach
• Self-Reflection in LLM Agents: Effects on Problem-Solving Performance
• Self-reflective Uncertainties: Do LLMs Know Their Internal Answer Distribution?
• Self-reinforcing cascades: A spreading model for beliefs or products of varying intensity or quality
• Self-Rewarding Language Models
• Self-Rewarding Vision-Language Model via Reasoning Decomposition
• Self-Supervised Models of Speech Infer Universal Articulatory Kinematics
• Self-Taught Evaluators
• Semantic Change Characterization with LLMs using Rhetorics
• Semantic Parsing for Task Oriented Dialog using Hierarchical Representations
• Semantic Specialization for Knowledge-based Word Sense Disambiguation
• Semantic Structure in Large Language Model Embeddings
• Sequence Organization in Interaction: A Primer in Conversation Analysis
• SERL: Self-Examining Reinforcement Learning on Open-Domain
• SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
• Shaping Explanations: Semantic Reward Modeling with Encoder-Only Transformers for GRPO
• Should Humans Lie to Machines?  The Incentive Compatibility of Lasso and General Weighted Lasso
• Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialogue
• ShowUI: One Vision-Language-Action Model for GUI Visual Agent
• Silence is Not Consensus: Disrupting Agreement Bias in Multi-Agent LLMs via Catfish Agent for Clinical Decision Making
• Simple Linguistic Inferences of Large Language Models (LLMs): Blind Spots and Blinds
• Simple Synthetic Data Reduces Sycophancy In Large Language Models
• SimPO: Simple Preference Optimization with a Reference-Free Reward
• Simulacra as conscious exotica
• Simulating Society Requires Simulating Thought
• Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets
• Single-agent or Multi-agent Systems? Why Not Both?
• Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates
• SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
• Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models
• Sleep-time Compute: Beyond Inference Scaling at Test-time
• Small Language Models are the Future of Agentic AI
• Small LLMs Are Weak Tool Learners: A Multi-LLM Agent
• SMILE: Evaluation and Domain Adaptation for Social Media Language Understanding
• Social Responses to Media Technologies in the 21st Century: The Media are Social Actors Paradigm
• Social Robots for Long-Term Interaction: A Survey
• Social Skill Training with Large Language Models
• SocraSynth: Multi-LLM Reasoning with Conditional Statistics
• Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space
• Soft Tokens, Hard Truths
• SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs
• SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching
• Solving a Million-Step LLM Task with Zero Errors
• SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents
• Sources of Hallucination by Large Language Models on Inference Tasks
• SParC: Cross-Domain Semantic Parsing in Context
• Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
• SPICE: Self-Play In Corpus Environments Improves Reasoning
• SpreadsheetLLM: Encoding Spreadsheets for Large Language Models
• Spurious Forgetting in Continual Learning of Language Models
• Spurious Rewards: Rethinking Training Signals in RLVR
• SSRL: Self-Search Reinforcement Learning
• Stance Detection on Social Media with Fine-Tuned Large Language Models
• Statistical and Algorithmic Foundations of Reinforcement Learning
• SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF
• StepWiser: Stepwise Generative Judges for Wiser Reasoning
• Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!
• Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
• Strategic Reasoning with Language Models
• Stream of Search (SoS): Learning to Search in Language
• Stress Testing Deliberative Alignment for Anti-Scheming Training
• StructGPT: A General Framework for Large Language Model to Reason over Structured Data
• StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization
• Structured and Natural Responses Co-generation for Conversational Search
• Study: Large language models can’t effectively recognize users’ motivation, but can support behavior change for those ready to act
• Style Vectors for Steering Generative Large Language Models
• Subliminal Learning: Language models transmit behavioral traits via hidden signals in data
• Summaries, Highlights, and Action items: Design, implementation and evaluation of an LLM-powered meeting recap system
• Supervised Pretraining Can Learn In-Context Reinforcement Learning
• SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning
• Supporting Physical Activity Behavior Change with LLM-Based Conversational Agents
• Suppressing Pink Elephants with Direct Principle Feedback
• Survey on Evaluation of LLM-based Agents
• Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
• Sycophancy Mitigation Through Reinforcement Learning with Uncertainty-Aware Adaptive Reasoning Trajectories
• Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians
• SymAgent: A Neural-Symbolic Self-Learning Agent Framework for Complex Reasoning over Knowledge Graphs
• Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models
• Synthetic Dialogue Dataset Generation using LLM Agents
• System 1 vs. System 2 Thinking
• System 2 Attention (is something you might need too)
• Systematic synthesis of design prompts for large language models in conceptual design
• Tailored Conversations beyond LLMs: A RL-Based Dialogue Manager
• TaleStream: Supporting Story Ideation with Trope Knowledge
• Talk Less, Interact Better: Evaluating In-context Conversational Adaptation in Multimodal LLMs
• Talk like a Graph: Encoding Graphs for Large Language Models
• Talking About Large Language Models
• TarGEN: Targeted Data Generation with Large Language Models
• Target-Guided Open-Domain Conversation
• Task Contamination: Language Models May Not Be Few-Shot Anymore
• Task-Oriented Dialogue as Dataflow Synthesis
• Task-Oriented Dialogue with In-Context Learning
• TaskLAMA: Probing the Complex Task Understanding of Language Models
• TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and Agent Generation
• Teaching Large Language Models to Reason with Reinforcement Learning
• Teaching Probabilistic Logical Reasoning to Transformers
• Tell me about yourself: LLMs are aware of their learned behaviors
• Temporal Self-Rewarding Language Models: Decoupling Chosen-Rejected via Past-Future
• Test-time Prompt Intervention
• Test-Time Scaling with Reflective Generative Model
• Textgrad: Automatic “Differentiation” via Text
• The Abstraction Fallacy: Why AI Can Simulate But Not Instantiate Consciousness
• The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs
• The Architectural Implications of Facebook’s DNN-based Personalized Recommendation
• The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
• The Art of Scaling Reinforcement Learning Compute for LLMs
• The Assistant Axis: Situating and Stabilizing the Default Persona of Language Models
• The Challenges in Designing a Prevention Chatbot for Eating Disorders: Observational Study
• The Consensus Game: Language Model Generation via Equilibrium Search
• The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a Reasoning Model will Think
• The Curse Of Recursion: Training On Generated Data Makes Models Forget
• The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind
• The Demon is in Ambiguity: Revisiting Situation Recognition with Single Positive Multi-Label Learning
• The Digital Therapeutic Alliance and Human-Computer Interaction
• The Digital Therapeutic Alliance: Prospects and Considerations
• The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation
• The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis
• The Emotion-Memory Link: Do Memorability Annotations Matter for Intelligent Systems?
• The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models
• The False Promise of Imitating Proprietary LLMs
• The Fellowship of the LLMs: Multi-Agent Workflows for Synthetic Preference Optimization Dataset Generation
• The Future of AI: Exploring the Potential of Large Concept Models
• The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs
• The Hallucination Tax of Reinforcement Finetuning
• The Hermeneutics of Artificial Text
• The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas
• The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs
• The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs
• The Illusion of the Illusion of the Illusion of Thinking
• The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
• The Impact of AI-Generated Text on the Internet
• The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers
• The Incomplete Bridge: How AI Research (Mis)Engages with Psychology
• The Insanity of Relying on Vector Embeddings: Why RAG Fails
• The Invisible Leash: Why RLVR May Not Escape Its Origin
• The Labor Market Effects of Generative Artificial Intelligence
• The Levers of Political Persuasion with Conversational AI
• The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows
• The Method of Critical AI Studies, A Propaedeutic
• The Missing Layer of AGI: From Pattern Alchemy to Coordination Physics
• The Model Says Walk: How Surface Heuristics Override Implicit Constraints in LLM Reasoning
• The Molecular Structure of Thought: Mapping the Topology of Long Chain-of-Thought Reasoning
• The Moral Turing Test: Evaluating Human-LLM Alignment in Moral Decision-Making
• The Netflix Recommender System: Algorithms, Business Value, and Innovation
• The Partner Modelling Questionnaire: A validated self-report measure of perceptions toward machines as dialogue partners
• The persuasive effects of political microtargeting in the age of generative artificial intelligence
• The Place of Emotion in Argument
• The Prompt Report: A Systematic Survey of Prompting Techniques
• The Return of Pseudosciences in Artificial Intelligence: Have Machine Learning and Deep Learning Forgotten Lessons from Statistics and History?
• The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"
• The Serial Scaling Hypothesis
• The social component of the projection behavior of clausal complement contents
• The state of enterprise AI
• The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning
• The Surprising Effectiveness of Test-Time Training for Abstract Reasoning
• The Thin Line Between Comprehension and Persuasion in LLMs
• The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities
• The Unreasonable Ineffectiveness of the Deeper Layers
• The Vanishing Gradient Problem for Stiff Neural Differential Equations
• The Vector Grounding Problem
• TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
• Theorem-of-Thought: A Multi-Agent Framework for Abductive, Deductive, and Inductive Reasoning in Language Models
• Theory of Knowledge Based on the Idea of the Discursive Space
• Theory of Mind abilities of Large Language Models in Human-Robot Interaction : An Illusion?
• Think before you speak: Training Language Models With Pause Tokens
• Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
• Think Deep, Think Fast: Investigating Efficiency of Verifier-free Inference-time-scaling Methods
• Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models
• Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLMs for Countering Hate
• Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection
• Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking
• Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memory
• Think-on-Graph: Deep and Responsible Reasoning of Large Language Model with Knowledge Graph
• Thinking Assistants: LLM-Based Conversational Assistants that Help Users Think By Asking rather than Answering
• Thinking Augmented Pre-training
• Thinking Forward and Backward: Effective Backward Planning with Large Language Models
• Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning
• Thinking Inside the Mask: In-Place Prompting in Diffusion LLMs
• Thinking LLMs: General Instruction Following with Thought Generation
• Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction
• Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender
• Thinkless: LLM Learns When to Think
• Thought Anchors: Which LLM Reasoning Steps Matter?
• Thought Communication in Multiagent Collaboration
• Thought Virus: Viral Misalignment via Subliminal Prompting in Multi-Agent Systems
• Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs
• Thoughts without Thinking: Reconsidering the Explanatory Value of Chain-of-Thought Reasoning in LLMs through Agentic Pipelines
• Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation
• Through the Lens of Human-Human Collaboration: A Configurable Research Platform for Exploring Human-Agent Collaboration
• Tina: Tiny Reasoning Models via LoRA
• Titans: Learning to Memorize at Test Time
• To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
• To Tell The Truth: Language of Deception and Language Models
• TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters
• Too Good to be Bad: On the Failure of LLMs to Role-Play Villains
• ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis
• Topic Modeling in Embedding Spaces
• Topic Shift Detection for Mixed Initiative Response
• Topic-Guided Conversational Recommender in Multiple Domains
• Topology of Reasoning: Understanding Large Reasoning Models through Reasoning Graph Properties
• Toward Conversational Agents with Context and Time Sensitive Long-term Memory
• Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
• Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks
• Toward understanding and preventing misalignment generalization
• Towards a Deeper Understanding of Reasoning Capabilities in Large Language Models
• Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models
• Towards a Science of Scaling Agent Systems
• Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs
• Towards Algorithmic Experience
• Towards Collective Superintelligence, a Pilot Study
• Towards Conversational Recommendation over Multi-Type Dialogs
• Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset
• Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
• Towards Healthy AI: Large Language Models Need Therapists Too
• Towards Human-centered Proactive Conversational Agents
• Towards Large Reasoning Models: A Survey on Scaling LLM Reasoning Capabilities
• Towards Machine Theory of Mind with Large Language Model-Augmented Inverse Planning
• Towards Monosemanticity: Decomposing Language Models With Dictionary Learning
• Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control
• Towards Question-based Recommender Systems
• Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models
• Towards Safe and Honest AI Agents with Neural Self-Other Overlap
• Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought
• Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models
• Train Long, Think Short: Curriculum Learning for Efficient Reasoning
• Training a Generally Curious Agent
• Training Dialogue Systems by AI Feedback for Improving Overall Dialogue Impression
• Training Language Models for Social Deduction with Multi-Agent Reinforcement Learning
• Training language models to be warm and empathetic makes them less reliable and more sycophantic
• Training language models to follow instructions with human feedback
• Training Language Models to Self-Correct via Reinforcement Learning
• Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
• Training Large Language Models to Reason in a Continuous Latent Space
• Training Long-Context, Multi-Turn Software Engineering Agents with Reinforcement Learning
• Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis
• Training-Free Group Relative Policy Optimization
• Transcendence: Generative Models Can Outperform The Experts That Train Them
• Transformer-based cynical expression detection in a corpus of Spanish YouTube reviews
• Transformer2: Self-adaptive LLMs
• Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
• TREC iKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistants
• Tree of Thoughts: Deliberate Problem Solving with Large Language Models
• Tree Search for Language Model Agents
• TreeRL: LLM Reinforcement Learning with On-Policy Tree Search
• Triggering Hallucinations in LLMs: A Quantitative Study of Prompt-Induced Hallucination in Large Language Models
• Truly Self-Improving Agents Require Intrinsic Metacognitive Learning
• Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods
• TrustLLM: Trustworthiness in Large Language Models
• Truth or lie: Exploring the language of deception
• TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning
• TTRL: Test-Time Reinforcement Learning
• Tube2Vec: Social and Semantic Embeddings of YouTube Channels
• Tulu 3: Pushing Frontiers in Open Language Model Post-Training
• Tuning Language Models by Proxy
• Turiya at DialAM-2024: Inference Anchoring Theory Based LLM Parsers
• Turn Every Application into an Agent: Towards Efficient Human-Agent-Computer Interaction with API-First LLM-Based Agents
• Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusion
• Turning large language models into cognitive models
• Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization
• TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models
• Typed-RAG: Type-aware Multi-Aspect Decomposition for Non-Factoid Question Answering
• UI-JEPA: Towards Active Perception of User Intent through Onscreen User Activity
• Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models
• Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy
• Understanding Before Reasoning: Enhancing Chain-of-Thought with Iterative Summarization Pre-Prompting
• Understanding Hidden Computations in Chain-of-Thought Reasoning
• Understanding the Role of User Profile in the Personalization of Large Language Models
• Understanding the Therapeutic Relationship between Counselors and Clients in Online Text-based Counseling using LLMs
• Understanding, explaining, and utilizing medical artificial intelligence
• Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning
• Unifying Large Language Models and Knowledge Graphs: A Roadmap
• Unifying Nearest Neighbors Collaborative Filtering
• UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs
• Universe of Thoughts: Enabling Creative Reasoning with Large Language Models
• Unleashing Cognitive Synergy In Large Language Models: A Task-solving Agent Through Multi-persona Self-collaboration
• Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem
• Unlocking Varied Perspectives: A Persona-Based Multi-Agent Framework with Debate-Driven Text Planning for Argument Generation
• Unsupervised Elicitation of Language Models
• Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study
• UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation
• UR2: Unify RAG and Reasoning through Reinforcement Learning
• User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal
• User-Centric Conversational Recommendation with Multi-Aspect User Modeling
• UserBench: An Interactive Gym Environment for User-Centric Agents
• Using Computational Models to Test Syntactic Learnability
• Using Large Language Models to Create AI Personas for Replication and Prediction of Media Effects: An Empirical Test of 133 Published Experimental Research Findings
• Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
• Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies
• Using Linguistic Synchrony to Evaluate Large Language Models for Cognitive Behavioral Therapy
• Using LLMs to Discover Legal Factors
• Using Natural Language for Reward Shaping in Reinforcement Learning
• Using Navigation to Improve Recommendations in Real-Time
• Using Topic Models to Identify Clients’ Functioning Levels and Alliance Ruptures in Psychotherapy
• Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs
• Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties
• Variational Autoencoders for Collaborative Filtering
• VCounselor: A Psychological Intervention Chat Agent Based on a Knowledge-Enhanced Large Language Model
• Verbal lie detection using Large Language Models
• Virtual Assistance in Any Context
• Virtuous Machines: Towards Artificial General Science
• VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction
• Voxtral
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• We Are All Creators: Generative AI, Collective Knowledge, and the Path Towards Human-AI Synergy
• We Wont be Missed: Work and Growth in the Era of AGI
• Weak-to-Strong GraphRAG: Aligning Weak Retrievers with Large Language Models for Graph-based Retrieval Augmented Generation
• Web-Browsing LLMs Can Access Social Media Profiles and Infer User Demographics
• Weight-sparse transformers have interpretable circuits
• We’re Afraid Language Models Aren’t Modeling Ambiguity
• What are the Goals of Distributional Semantics?
• What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT
• What does it mean to understand language?
• What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity
• What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models
• What is a Discourse Graph?
• What Makes a Good Natural Language Prompt?
• What the F*ck Is Artificial General Intelligence?
• What we talk to when we talk to language models
• When AIs Judge AIs: The Rise of Agent-as-a-Judge Evaluation for LLMs
• When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language Models
• When Large Language Models are More Persuasive Than Incentivized Humans, and Why
• When Large Language Models contradict humans? Large Language Models’ Sycophantic Behaviour
• When More is Less: Understanding Chain-of-Thought Length in LLMs
• When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions
• When Reject Turns into Accept: Quantifying the Vulnerability of LLM-Based Scientific Reviewers to Indirect Prompt Injection
• When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMs
• WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent Triggerability in Task-Oriented Dialogue
• Where to show Demos in Your Prompt: A Positional Bias of In-Context Learning
• Who’s Afraid of (Left) Hyperstitions
• Why Do Multi-agent LLM Systems Fail?
• Why Do People Rate? Theory and Evidence on Online Ratings
• Why Do Some Language Models Fake Alignment While Others Don't?
• Wide & Deep Learning for Recommender Systems
• Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness
• Witness
• Word Meanings in Transformer Language Models
• Working Alliance Transformer for Psychotherapy Dialogue Classification
• Working with AI: Measuring the Occupational Implications of Generative AI
• Workplace Everyday-Creativity through a Highly-Conversational UI to Large Language Models
• Writing-Zero: Bridge the Gap Between Non-verifiable Tasks and Verifiable Rewards
• You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures
• Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
• ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning
• Zero-Shot Verification-guided Chain of Thoughts
• ZeroSearch: Incentivize the Search Capability of LLMs without Searching
• “Hello There! Is Now a Good Time to Talk?”: Opportune Moments for Proactive Interactions with Smart Speakers
• “It Felt Like Having a Second Mind”: Investigating Human-AI Co-creativity in Prewriting with Large Language Models
• “Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations
• “Understanding AI”: Semantic Grounding in Large Language Models
• “What do others think?”: Task-Oriented Conversational Modeling with Subjective Knowledge
• 𝙻𝙼𝟸: A Simple Society of Language Models Solves Complex Reasoning