5 papers
TriggerBench: Investigating Prospective Memory for Large Language Models
Tianhua Zhang, Xinjiang Wang, Qianxi Zhang +6
While Large Language Models (LLMs) are increasingly deployed in long interactions, existing evaluations focus predominantly on retrospective memory (RM) via explicit queries. Prosp…
Agentic Cognitive Profiling: Realigning Automated Alzheimer's Disease Detection with Clinical Construct Validity
Jiawen Kang, Kun Li, Dongrui Han +5
Automated Alzheimer's Disease (AD) screening has predominantly followed the inductive paradigm of pattern recognition, which directly maps the input signal to the outcome label. Th…
TreePS-RAG: Tree-based Process Supervision for Reinforcement Learning in Agentic RAG
Tianhua Zhang, Kun Li, Junan Li +5
Agentic retrieval-augmented generation (RAG) formulates question answering as a multi-step interaction between reasoning and information retrieval, and has recently been advanced b…
RAG-Zeval: Towards Robust and Interpretable Evaluation on RAG Responses through End-to-End Rule-Guided Reasoning
Kun Li, Yunxiang Li, Tianhua Zhang +4
Robust evaluation is critical for deploying trustworthy retrieval-augmented generation (RAG) systems. However, current LLM-based evaluation frameworks predominantly rely on directl…
Generate, Discriminate, Evolve: Enhancing Context Faithfulness via Fine-Grained Sentence-Level Self-Evolution
Kun Li, Tianhua Zhang, Yunxiang Li +5
Improving context faithfulness in large language models is essential for developing trustworthy retrieval augmented generation systems and mitigating hallucinations, especially in…