40 citations · 58 across the 11 of their papers we have counts for
13 papers
When Words Fall Short: Iterative Synergy Between Verbalized Reasoning and Hidden Features for LLM Confidence Estimation
Yekun Xu, Ante Wang, Jingyi Ren +3
Confidence estimation is crucial for developing trustworthy large language models (LLMs), with most methods following estimator-based or verbalization-based paradigms. While recent…
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…
Enhancing LLM Metacognition via Cognitive Pairwise Training
Weitao Li, Hao Zhou, Xuanyu Lei +11
Reinforcement learning with verifiable rewards (RLVR) has become central to LLM reasoning, but its outcome-level rewards can make models more willing to give confident answers when…
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…
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…
Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning
Yunghwei Lai, Kaiming Liu, Ziyue Wang +2
The professionalism of a human doctor in outpatient service depends on two core abilities: the ability to make accurate medical decisions and the medical consultation skill to cond…
Patient-Zero: Scaling Synthetic Patient Agents to Real-World Distributions without Real Patient Data
Yunghwei Lai, Ziyue Wang, Weizhi Ma +1
Synthetic data generation with Large Language Models (LLMs) has emerged as a promising solution in the medical domain to mitigate data scarcity and privacy constraints. However, ex…
UR: Unify RAG and Reasoning through Reinforcement Learning
Weitao Li, Boran Xiang, Xiaolong Wang +5
Large Language Models (LLMs) have shown strong capabilities through two complementary paradigms: Retrieval-Augmented Generation (RAG) for knowledge grounding and Reinforcement Lear…
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…
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…
Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents
Junkai Li, Yunghwei Lai, Weitao Li +8
The recent rapid development of large language models (LLMs) has sparked a new wave of technological revolution in medical artificial intelligence (AI). While LLMs are designed to…
Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification
Jimmy Lin, Junkai Li, Jiasi Gao +2
Tactile signals collected by wearable electronics are essential in modeling and understanding human behavior. One of the main applications of tactile signals is action classificati…
Citation-Enhanced Generation for LLM-based Chatbots
Weitao Li, Junkai Li, Weizhi Ma +1
Large language models (LLMs) exhibit powerful general intelligence across diverse scenarios, including their integration into chatbots. However, a vital challenge of LLM-based chat…