40 citations · 90 across the 14 of their papers we have counts for
15 papers
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…
Beyond "I Don't Know": Evaluating LLM Self-Awareness in Discriminating Data and Model Uncertainty
Jingyi Ren, Ante Wang, Yunghwei Lai +5
Reliable Large Language Models (LLMs) should abstain when confidence is insufficient. However, prior studies often treat refusal as a generic "I don't know'', failing to distinguis…
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…
StoryBench: A Dynamic Benchmark for Evaluating Long-Term Memory with Multi Turns
Luanbo Wan, Weizhi Ma
Long-term memory (LTM) is essential for large language models (LLMs) to achieve autonomous intelligence in complex, evolving environments. Despite increasing efforts in memory-augm…
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…
Long Term Memory: The Foundation of AI Self-Evolution
Xun Jiang, Feng Li, Han Zhao +12
Large language models (LLMs) like GPTs, trained on vast datasets, have demonstrated impressive capabilities in language understanding, reasoning, and planning, achieving human-leve…
Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering
Yihong Wu, Le Zhang, Fengran Mo +3
Graph-based models and contrastive learning have emerged as prominent methods in Collaborative Filtering (CF). While many existing models in CF incorporate these methods in their d…
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…