Dialog Topics and Modeling

Related topics:

• A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects
  Proactive dialogue systems, related to a wide range of real-world conversational applications, equip the conversational agent with the capability of leading the conversation direction towards achievin…
• Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning
  Large language models (LLMs) often fail to ask effective questions under uncertainty, making them unreliable in domains where proactive information-gathering is essential for decision-making. We prese…
• Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification
  “Aspect-based sentiment classification is a crucial problem in fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspect according to its context. Previous work…
• CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues
  [**https://arxiv.org/abs/2404.03820**](https://arxiv.org/abs/2404.03820) Recent advancements in instruction-tuning datasets have predominantly focused on specific tasks like mathematical or logical…
• Characterizing Online Discussion Using Coarse Discourse Sequences
  As more social interaction takes place online, researchers have become interested in studying the discourse occurring in online social media. From these studies, researchers can examine how people con…
• Clarifying the Path to User Satisfaction: An Investigation into Clarification Usefulness
  Several models are proposed in the ConvAI3 challenge (Aliannejadi et al., 2020), aiming to incorporate CQs in the ranking process, mostly proposed based on pre-trained language models. Complementing t…
• Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations
  In the field of natural language processing, open-domain chatbots have emerged as an important research topic. However, a major limitation of existing open-domain chatbot research is its singular focu…
• Conversational Alignment with Artificial Intelligence in Context
  The development of sophisticated artificial intelligence (AI) conversational agents based on large language models raises important questions about the relationship between human norms, values, and pr…
• Conversational Semantic Parsing for Dialog State Tracking
  We consider a new perspective on dialog state tracking (DST), the task of estimating a user’s goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical repre…
• Conversations Gone Awry: Detecting Early Signs of Conversational Failure
  One of the main challenges online social systems face is the prevalence of antisocial behavior, such as harassment and personal attacks. In this work, we introduce the task of predicting from the very…
• DAPIE:  Interactive  Step-by-Step  Explanatory  Dialogues  to  Answer  Children’s  Why  and  How  Questions
  Description automatically generated](file:////Users/adrianchan/Library/Group%20Containers/UBF8T346G9.Office/TemporaryItems/msohtmlclip/clip_image007.png) To identify effective techniques for answerin…
• Dialog Inpainting: Turning Documents into Dialogs
  Many important questions (e.g. “How to eat healthier?”) require conversation to establish context and explore in depth. However, conversational question answering (ConvQA) systems have long been stymi…
• Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources
  https:// CGMI: Configurable General Multi-Agent Interaction Framework [https://arxiv.org/abs/2308.12503](https://arxiv.org/abs/2308.12503) [[Memory]] [[Role Play]] “With the capabilities of large…
• Diplomat: A Dialogue Dataset for Situated PragMATic Reasoning
  “We introduce a new benchmark, Diplomat, aiming at a unified paradigm for pragmatic reasoning and situated conversational understanding. Compared with previous works that treat different figurative ex…
• Discursive Socratic Questioning: Evaluating the Faithfulness of Language Models’ Understanding of Discourse Relations
  While large language models have significantly enhanced the effectiveness of discourse relation classifications, it remains unclear whether their comprehension is faithful and reliable. We provide DIS…
• DiscussLLM: Teaching Large Language Models When to Speak
  Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating human-like text, yet they largely operate as reactive agents, responding only when directly promp…
• Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection
  In this paper, we study the task of selecting the optimal response given a user and system utterance history in retrieval-based multi-turn dialog systems. Recently, pre-trained language models (e.g., …
• Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search
  The future of conversational agents will provide users with personalized information responses. However, a significant challenge in developing models is the lack of large-scale dialogue datasets that …
• Empirical Study of Symmetrical Reasoning in Conversational Chatbots
  This work explores the capability of conversational chatbots powered by large language models (LLMs), to understand and characterize predicate symmetry, a cognitive linguistic function traditionally b…
• Empowering Domain-Specific Language Models with Graph-Oriented Databases: A Paradigm Shift in Performance and Model Maintenance
  Modern GODB have emerged as a solution for highly-connected data, and link oriented queries and algorithms [2]. In fact, they have been a valuable solution in software industry for decades. The implem…
• Evaluating Emotional Nuances In Dialogue Summarization
  “Affective content has been the target of a few summarization tasks such as opinion summarization [Wang and Ling, 2016]. However, opinion is only a subset of affective expressions and such task mainly…
• Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences
  Decision conferences are structured, collaborative meetings that bring together experts from various fields to address complex issues and reach a consensus on recommendations for future actions or pol…
• From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models
  RAISE, an enhancement of the ReAct framework, incorporates a dual-component memory system, mirroring human short-term and long-term memory, to maintain context and continuity in conversations. It enta…
• Generative Agents: Interactive Simulacra of Human Behavior
  “Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, …
• Grounding Gaps in Language Model Generations
  However, it is unclear whether large language models (LLMs) generate text that reflects human grounding. To this end, we curate a set of grounding acts and propose corresponding metrics that quantify …
• IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI Systems
  The system pipeline consists of the following steps: (1) The IntellAgent system receives a schema of the system database along with either a chatbot system prompt or a document outlining the company p…
• Intent Mismatch Causes LLMs to Get Lost in Multi-Turn Conversation
  Multi-turn conversation has emerged as a predominant interaction paradigm for Large Language Models (LLMs). Users often employ follow-up questions to refine their intent, expecting LLMs to adapt dynam…
• Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues
  we focus on selecting answers, which aims to identify the correct answer from a pool of candidates given a dialogue context. Typically, there are two main branches of approaches to produce answers, i.…
• LLMs Get Lost In Multi-Turn Conversation
  Large Language Models (LLMs) are conversational interfaces. As such, LLMs have the potential to assist their users not only when they can fully specify the task at hand, but also to help them define, …
• Learning Retrieval Augmentation for Personalized Dialogue Generation
  Personalized dialogue generation, focusing on generating highly tailored responses by leveraging persona profiles and dialogue context, has gained significant attention in conversational AI applicatio…
• Lexical Entrainment for Conversational Systems
  lexical entrainment (LE), a phenomenon in which speakers in human-human conversations tend to naturally and subconsciously align their lexical choices with those of their interlocutors, leading to mor…
• Linguistic Alignment in Conversational AI: A Systematic Review of Cognitive-Linguistic Dimensions, Measurements, and User Outcomes (2020–2025)
  Conversational Artificial Intelligence systems frequently adapt to or mirror the user’s linguistic style, an emergent dynamic that shapes whether the AI is perceived as a tool, a partner, or a hybrid …
• MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation
  “We propose MemoChat, a pipeline for refining instructions that enables large language models (LLMs) to effectively employ self-composed memos for maintaining consistent long-range open-domain convers…
• Memory Sandbox: Transparent and Interactive Memory Management for Conversational Agents
  “Large Language Models (LLMs) are currently capable of generating human-like responses in open-domain tasks [4]. This has led to a new generation of conversational agents, such as chatGPT, which are n…
• Modeling the Quality of Dialogical Explanations
  Expert explainers usually plan an explanation strategy by choosing appropriate explanation moves, dialogue acts, and topics to ensure optimal comprehension on the explainee side (Wachsmuth and Alshoma…
• OpinionConv: Conversational Product Search with Grounded Opinions
  “When searching for products, the opinions of others play an important role in making informed decisions. Subjective experiences about a product can be a valuable source of information. This is also t…
• Post-training for Efficient Communication via Convention Formation
  Humans communicate with increasing efficiency in multi-turn interactions, by adapting their language and forming ad-hoc conventions. In contrast, prior work shows that LLMs do not naturally show this …
• Proactive Conversational Agents with Inner Thoughts
  In this paper, we demonstrate the limitations of such methods and rethink what it means for AI to be proactive in multi-party, human-AI conversations. We propose that just like humans, rather than mer…
• Proactive Moderation of Online Discussions: Existing Practices and the Potential for Algorithmic Support
  Multiple studies on content moderation have identified a problem of scale: even if antisocial behavior is a small fraction of all content that gets posted, the sheer size of modern online platforms, t…
• Prompted LLMs as Chatbot Modules for Long Open-domain Conversation
  At the start of a conversation, a pre-defined persona is stored in the memory pool. When a user sends a message, the clarifier rephrases it to resolve any ambiguities and passes it to the DPR model wh…
• SDPO: Segment-Level Direct Preference Optimization for Social Agents
  Social agents powered by large language models (LLMs) can simulate human social behaviors but fall short in handling complex goal-oriented social dialogues. Direct Preference Optimization (DPO) has pr…
• SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphs
  Knowledge-based conversational question answering (KBCQA) confronts persistent challenges in resolving coreference, modeling contextual dependencies, and executing complex logical reasoning. Existing …
• SMILE: Evaluation and Domain Adaptation for Social Media Language Understanding
  “Social media (SM) plays an increasingly important role in our lives. As of 2021, seven out of ten US adults use at least one social media platform like Facebook, Twitter, Instagram, or Pinterest [3].…
• Silence is Not Consensus: Disrupting Agreement Bias in Multi-Agent LLMs via Catfish Agent for Clinical Decision Making
  Large language models (LLMs) have demonstrated strong potential in clinical question answering, with recent multi-agent frameworks further improving diagnostic accuracy via collaborative reasoning. Ho…
• Talk Less, Interact Better: Evaluating In-context Conversational Adaptation in Multimodal LLMs
  Humans spontaneously use increasingly efficient language as interactions progress, by adapting and forming ad-hoc conventions. This phenomenon has been studied extensively using reference games, showi…
• Target-Guided Open-Domain Conversation
  Many real-world open-domain conversation applications have specific goals to achieve during open-ended chats, such as recommendation, psychotherapy, education, etc. We study the problem of imposing co…
• Task-Oriented Dialogue as Dataflow Synthesis
  We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph. A dialogue agent maps each user utterance to a program that extends this graph. Programs i…
• Task-Oriented Dialogue with In-Context Learning
  We describe a system for building task oriented dialogue systems combining the in context learning abilities of large language models (LLMs) with the deterministic execution of business logic. LLMs ar…
• TaskLAMA: Probing the Complex Task Understanding of Language Models
  “Structured Complex Task Decomposition (SCTD) is the problem of breaking down a complex real-world task (such as planning a wedding) into a directed acyclic graph over individual steps that contribute…
• The Levers of Political Persuasion with Conversational AI
  There are widespread fears that conversational AI could soon exert unprecedented influence over human beliefs. Here, in three large-scale experiments (N=76,977), we deployed 19 LLMs—including some pos…
• Training Dialogue Systems by AI Feedback for Improving Overall Dialogue Impression
  it is important to evaluate not only each response but also the user’s overall dialogue impression. For example, improving the dialogue system’s consistency of responses, personality, and empathy will…
• “Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations
  we present a first corpus for computational research on how to explain in dialogues (Section 3). Where possible, we followed the literature, but the lack of research on human interaction in explainin…