Conversational Agents

Related topics:

• AInsight: Augmenting Expert Decision-Making with On-the-Fly Insights Grounded in Historical Data
  In decision-making conversations, experts must navigate complex choices and make on-the-spot decisions while engaged in conversation. Although extensive historical data often exists, the real-time nat…
• Attentive Reasoning Queries: A Systematic Method for Optimizing Instruction-Following in Large Language Models
  We present Attentive Reasoning Queries (ARQs), a novel structured reasoning approach that significantly improves instruction-following in Large Language Models through domain-specialized reasoning blu…
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
  “This technical report presents AutoGen,1 a new framework that enables development of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen agents are custom…
• Beyond Passive Critical Thinking: Fostering Proactive Questioning to Enhance Human-AI Collaboration
  Critical thinking is essential for building robust AI systems, preventing them from blindly accepting flawed data or biased reasoning. However, prior work has primarily focused on passive critical thi…
• Bridging the gulf of envisioning: Cognitive design challenges in llm interfaces.
  Large language models (LLMs) exhibit dynamic capabilities and appear to comprehend complex and ambiguous natural language prompts. However, calibrating LLM interactions is challenging for interface de…
• Building Persona Consistent Dialogue Agents with Offline Reinforcement Learning
  Maintaining a consistent persona is a key quality for any open domain dialogue system. Current state-of-the-art systems do this by training agents with supervised learning or online reinforcement lear…
• CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
  The rapid advancement of chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be…
• Can AI Have a Personality? Prompt Engineering for AI Personality Simulation: A Chatbot Case Study in Gender-Affirming Voice Therapy Training
  Abstract—This thesis investigates whether large language models (LLMs) can be guided to simulate a consistent personality through prompt engineering. The study explores this concept within the context…
• Challenges of Large Language Models for Mental Health Counseling
  large language models (LLMs) capable of understanding and generating human-like text may be used in supporting or providing psychological counseling. However, the application of LLMs in the mental hea…
• CollabLLM: From Passive Responders to Active Collaborators
  Large Language Models are typically trained with next-turn rewards, limiting their ability to optimize for long-term interaction. As a result, they often respond passively to ambiguous or open-ended u…
• Consistently Simulating Human Personas with Multi-Turn Reinforcement Learning
  Large Language Models (LLMs) are increasingly used to simulate human users in interactive settings such as therapy, education, and social role-play. While these simulations enable scalable training an…
• Conversational DNA: A New Visual Language for Understanding Dialogue Structure in Human and AI
  What if the patterns hidden within dialogue reveal more about communication than the words themselves? We introduce Conversational DNA, a novel visual language that treats any dialogue – whether betwe…
• DialogueReason: Rule-Based RL Sparks Dialogue Reasoning in LLMs
  We propose DialogueReason, a reasoning paradigm that uncovers the lost roles in monologue-style reasoning models, aiming to boost diversity and coherency of the reasoning process. Recent advances in R…
• Enhancing personalized multi-turn dialogue with curiosity reward
  Current training methods like Reinforcement Learning from Human Feedback (RLHF) prioritize helpfulness and safety but fall short in fostering truly empathetic, adaptive, and personalized interactions.…
• From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?
  While existing benchmarks probe the reasoning abilities of large language models (LLMs) across diverse domains, they predominantly assess passive reasoning, providing models with all the information n…
• GRASP: Municipal Budget AI Chatbots for Enhancing Civic Engagement
  Abstract—There are a growing number of AI applications, but none tailored specifically to help residents answer their questions about municipal budget, a topic most are interested in but few have a so…
• Goal Alignment in LLM-Based User Simulators for Conversational AI
  While current Large Language Models (LLMs) have advanced user simulation capabilities, we reveal that they struggle to consistently demonstrate goal-oriented behavior across multiturn conversations–a …
• Hello Again! LLM-powered Personalized Agent for Long-term Dialogue
  Open-domain dialogue systems have seen remarkable advancements with the development of large language models (LLMs). Nonetheless, most existing dialogue systems predominantly focus on brief single-ses…
• 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…
• Interaction Dynamics as a Reward Signal for LLMs
  The alignment of Large Language Models (LLMs) for multi-turn conversations typically relies on reward signals derived from the content of the text. This approach, however, overlooks a rich, complement…
• Interactions with generative AI chatbots: unveiling dialogic dynamics, students’ perceptions, and practical competencies in creative problem-solving
  The assigned CPS task was the creation of an innovative research proposal. We found that there were significant differences in the dialogic exchanges observed between the two types of interaction. Stu…
• Learning "Partner-Aware" Collaborators in Multi-Party Collaboration
  Large Language Models (LLMs) are increasingly bring deployed in agentic settings where they act as collaborators with humans. Therefore, it is increasingly important to be able to evaluate their abili…
• No that's not what I meant: Handling Third Position Repair in Conversational Question Answering
  The ability to handle miscommunication is crucial to robust and faithful conversational AI. People usually deal with miscommunication immediately as they detect it, using highly systematic interaction…
• PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking
  We present PolyResponse, a conversational search engine that supports task-oriented dialogue. It is a retrieval-based approach that bypasses the complex multi-component design of traditional task-orie…
• Proactive Conversational Agents in the Post-ChatGPT World
  Although astonished by their human-like performance, we find they share a significant weakness with many other existing conversational agents in that they all take a passive approach in responding to …
• Proactive behavior in voice assistants: A systematic review and conceptual model
  Yet, there is a lack of review studies synthesizing the current knowledge on how proactive behavior has been implemented in VAs and under what conditions proactivity has been found more or less suitab…
• Quantifying Human-AI Synergy
  We introduce a novel Bayesian Item Response Theory framework to quantify human– AI synergy, separating individual and collaborative ability while controlling for task difficulty in interactive setting…
• Reinforcement Learning for Optimizing RAG for Domain Chatbots
  Large Language Models (LLM), conversational assistants have become prevalent for domain use cases. LLMs acquire the ability to contextual question answering through extensive training, and Retrieval A…
• Rethinking Conversational Agents in the Era of LLMs: Proactivity, Non-collaborativity, and Beyond
  as LLMs are trained to follow users’ instructions, LLM-augmented conversational systems typically overlook the design of an essential property in intelligent conversations, i.e., goal awareness. In th…
• SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching
  We present a new method, SOLOIST,1 that uses transfer learning and machine teaching to build task bots at scale. We parameterize classical modular task-oriented dialog systems using a Transformer-base…
• Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians
  “AI psychosis” or “delusional spiraling” is an emerging phenomenon where AI chatbot users find themselves dangerously confident in outlandish beliefs after extended chatbot conversations. This phenome…
• Synthetic Dialogue Dataset Generation using LLM Agents
  Linear programming (LP) problems are pervasive in real-life applications. However, despite their apparent simplicity, an untrained user may find it difficult to determine the linear model of their spe…
• TREC iKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistants
  The collection contains a set of 36 personalized dialogues over 20 different topics each coupled with a Personal Text Knowledge Base (PTKB) that defines the bespoke user personas. A total of 344 turns…
• Thinking Assistants: LLM-Based Conversational Assistants that Help Users Think By Asking rather than Answering
  complex tasks like research and strategic thinking often benefit from a more comprehensive approach to augmenting the thinking process rather than passively getting information. We introduce the conce…
• Toward Conversational Agents with Context and Time Sensitive Long-term Memory
  There has recently been growing interest in conversational agents with long-term memory which has led to the rapid development of language models that use retrieval-augmented generation (RAG). Until r…
• Towards Human-centered Proactive Conversational Agents
  Recent research on proactive conversational agents (PCAs) mainly focuses on improving the system’s capabilities in anticipating and planning action sequences to accomplish tasks and achieve goals befo…
• Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models
  This paper proposes a systematic approach to examine the efficacy of domain knowledge and large language models (LLMs) in better representing conversations between a crisis counselor and a help seeker…
• Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models
  In this work, we introduce Uncertainty of Thoughts (UoT), an algorithm to augment large language models with the ability to actively seek information by asking effective questions. UoT combines 1) an …
• User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal
  Once language models (LMs) are deployed, they can interact with users long-term, ideally evolving continuously based on their feedback. Asking for direct user feedback can be disruptive; thus, we stud…
• Virtual Assistance in Any Context
  Abstract Several domain-specific assistants in the form of chatbots have conquered many commercial and private areas. However, there is still a limited level of systematic knowledge of the distinctive…
• Voxtral
  We present Voxtral Mini and Voxtral Small, two multimodal audio chat models. Voxtral is trained to comprehend both spoken audio and text documents, achieving state-of-the-art performance across a dive…
• WHEN TO ACT, WHEN TO WAIT: Modeling Structural Trajectories for Intent Triggerability in Task-Oriented Dialogue
  Task-oriented dialogue systems often face difficulties when user utterances seem semantically complete but lack necessary structural information for appropriate system action. This arises because user…
• Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness
  We explore the task of improving persona consistency of dialogue agents. Recent models tackling consistency often train with additional Natural Language Inference (NLI) labels or attach trained extra …