7 papers · 1 filter
Don't Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking
Ante Wang, Jiaqi Fu, Xuanyi Chen +4
Thinking has emerged as a critical capability for Large Language Models (LLMs) tackling complex tasks. However, its reactive nature, where reasoning is passively triggered only upo…
UR: Unify RAG and Reasoning through Reinforcement Learning
Weitao Li, Boran Xiang, Xiaolong Wang +3
Large Language Models (LLMs) have shown strong capabilities through two complementary paradigms: Retrieval-Augmented Generation (RAG) for knowledge grounding and Reinforcement Lear…
Let the Model Distribute Its Doubt: Confidence Estimation through Verbalized Probability Distribution
Ante Wang, Weizhi Ma, Yang Liu
Knowing the reliability of a model's response is essential in practical applications. Given the strong generation capabilities of large language models (LLMs), research has focused…
Towards Transparent RAG: Fostering Evidence Traceability in LLM Generation via Reinforcement Learning
Jingyi Ren, Yekun Xu, Xiaolong Wang +4
Retrieval-Augmented Generation (RAG) delivers substantial value in knowledge-intensive applications. However, its generated responses often lack transparent reasoning paths that tr…
The Dialogue That Heals: A Comprehensive Evaluation of Doctor Agents' Inquiry Capability
Linlu Gong, Ante Wang, Yunghwei Lai +2
An effective physician should possess a combination of empathy, expertise, patience, and clear communication when treating a patient. Recent advances have successfully endowed AI d…
Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation
Weitao Li, Kaiming Liu, Xiangyu Zhang +3
Retrieval-Augmented Generation (RAG) has emerged as a widely adopted approach for knowledge injection during large language model (LLM) inference in recent years. However, due to t…