Papers Dialogue Generation
“Dialogue Generation” 태그가 달린 논문 679편 · 필터 해제
Controlling and Assessing Appropriate Persona Use in LLM-based Dialogue Generation
In persona-based dialogue generation (PDG), LLMs often overuse persona attributes by incorporating them regardless of dialogue context, resulting in unnatural responses. Despite its practical significance, the underlying…
Response GenerationDialogue GenerationStranger, Fan, or Peer? A Systematic Study on the Role of Interlocutor in Persona-Based Dialogue Generation
Persona-based dialogue systems are usually conditioned on speaker biography, but dialogues involve at least two participants, and who has access to whose biography can vary across training, inference, and evaluation. Pri…
Dialogue GenerationEmoTrace: An Emotion Trajectory-Centered Framework for Psychological Support Dialogue Generation
Using large language models (LLMs) to assist psychological counseling is an important task in the field of natural language processing. The construction of high-quality psychological support dialogue corpora serves as a …
Dialogue GenerationIndicTalk: A Large-Scale Persona-Based Multilingual Conversational Corpus for Indic Languages
Large Language Models (LLMs) have transformed conversational AI, yet high-quality multilingual code-mixed dialogue resources remain scarce, particularly for Indic languages where speakers naturally alternate between Engl…
Dialogue GenerationConversational Human Audio-visual Talking Dialogue Generation
Large-scale dyadic interactive audio-visual dialogue (DIAD) datasets provide fundamental data resources for developing humanoid interactive virtual agents and digital humans. However, collecting such data is time-consumi…
Dialogue GenerationPRIDE: Privileged Information-enhanced Distillation for Empathetic Dialogue Generation
Large language models have demonstrated significant capabilities in generating diverse and context-aware responses for empathetic dialogue. However, their computational demands severely limit their deployment in resource…
Knowledge DistillationDialogue GenerationThe Significance of Style Diversity in Annotation-Free Synthetic Data Generation
Generating high-utility synthetic data for intent classification typically requires human-annotated seed data, which is often unavailable in fast-paced industrial settings. In this paper, we propose a framework for synth…
Synthetic Data GenerationIntent ClassificationDialogue GenerationEIBench: A Simulator-Based Benchmark and Turn-Credit RL for Emotion Management
Emotional intelligence (EI) in Large Language Models (LLMs) is often evaluated through static understanding tasks or single-response dialogue generation. However, emotion management is interactive: a good model should no…
Emotional IntelligenceDialogue GenerationPRISM: Prosody-Integrated Multi-Agent Reasoning Framework for Empathetic Spoken Dialogue
Empathetic spoken dialogue systems require not only semantically appropriate responses but also emotionally aligned prosodic expression. However, cascade pipelines often discard acoustic cues during speech-to-text conver…
Dialogue GenerationResponse GenerationSpeech SynthesisContext-Driven Incremental Compression for Multi-Turn Dialogue Generation
Modern conversational agents condition on an ever-growing dialogue history at each turn, incurring redundant attention and encoding costs that grow with conversation length. Naive truncation or summarization degrades fid…
Dialogue GenerationUXBench: Benchmarking User Experience in AI Assistants
As AI assistants serve millions of users daily, evaluating user experience (UX) beyond general model capability has become increasingly important. We present UXBench, the first user-centric benchmark grounded in real use…
Dialogue GenerationSafeCtrl-RL: Inference-Time Adaptive Behaviour Control for LLM Dialogue via RL-Driven Prompt Optimisation
Ensuring safe and contextually appropriate behaviour in Large Language Models (LLMs) remains a critical challenge for real-world deployment. We present \textbf{SafeCtrl-RL}, an inference-time behavioural control framewor…
Reinforcement LearningDialogue GenerationPolite on the Surface, Broken in Practice: A Curated Dataset for Fixing Generation and Register Failures in Low-Resource Bangla Text Generation
Recent advances in Multilingual Large Language Models (MLLMs) have significantly enhanced cross-lingual conversational capabilities, yet modeling culturally nuanced and context-dependent communication remains a critical …
parameter-efficient fine-tuningDialogue GenerationText GenerationMA$^{2}$P: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasion
Persuasive dialogue generation plays a vital role in decision-making, negotiation, counseling, and behavior change, yet it remains a challenging problem. In complex persuasion where the persuadee's internal states are no…
Dialogue GenerationXPERT: Expert Knowledge Transfer for Effective Training of Language Models
Mixture-of-Experts (MoE) language models organize knowledge into explicitly routed expert modules, making expert-level representations traceable and analyzable. By analyzing expert activation patterns in MoE large langua…
Dialogue GenerationCustomerSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators
We present CustomerSim, an environment and benchmark to evaluate the extent to which Multimodal Large Language Models (MLLMs) can simulate realistic, persona-driven customer behavior in chat-based retail environments. Wh…
Reinforcement LearningDialogue GenerationControllable Spoken Dialogue Generation: An LLM-Driven Grading System for K-12 Non-Native English Learners
Large language models (LLMs) often fail to meet the pedagogical needs of K-12 English learners in non-native contexts due to a proficiency mismatch. To address this widespread challenge, we introduce a proficiency-aligne…
Dialogue GenerationGraph2Counsel: Clinically Grounded Synthetic Counseling Dialogue Generation from Client Psychological Graphs
Rising demand for mental health support has increased interest in using Large Language Models (LLMs) for counseling. However, adapting LLMs to this high-risk safety-critical domain is hindered by the scarcity of real-wor…
Dialogue GenerationComPASS: Towards Personalized Agentic Social Support via Tool-Augmented Companionship
Developing compassionate interactive systems requires agents to not only understand user emotions but also provide diverse, substantive support. While recent works explore empathetic dialogue generation, they remain limi…
Dialogue GenerationStoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation
Large language models (LLMs) can generate fluent dialogue, but prior works lack situational grounding, dynamic strategy control, and evaluation aligned with clinical standards in motivational interviewing (MI). We introd…
Dialogue Generation