most citedCounselBench: A Large-Scale Expert Evaluation and Adversarial Benchmarking of Large Language Models in Mental Health Question Answering

3 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL20263 cited

CounselBench: A Large-Scale Expert Evaluation and Adversarial Benchmarking of Large Language Models in Mental Health Question Answering

Yahan Li, Jifan Yao, John Bosco S. Bunyi +3

Medical question answering (QA) benchmarks often focus on multiple-choice or fact-based tasks, leaving open-ended answers to real patient questions underexplored. This gap is parti…

cs.CL2026

MedExAgent: Training LLM Agents to Ask, Examine, and Diagnose in Noisy Clinical Environments

Yicheng Gao, Xiaolin Zhou, Yahan Li +2

Real-world clinical diagnosis is a complex process in which the doctor is required to obtain information from both interaction with the patient and conducting medical exams. Additi…

cs.CL2026

CounselReflect: A Toolkit for Auditing Mental-Health Dialogues

Yahan Li, Chaohao Du, Zeyang Li +5

Mental-health support is increasingly mediated by conversational systems (e.g., LLM-based tools), but users often lack structured ways to audit the quality and potential risks of t…

cs.CL2026

Beyond Idealized Patients: Evaluating LLMs under Challenging Patient Behaviors in Medical Consultations

Yahan Li, Xinyi Jie, Wanjia Ruan +5

Large language models (LLMs) are increasingly used for medical consultation and health information support. In this high-stakes setting, safety depends not only on medical knowledg…

cs.CL2026

Fairness or Fluency? An Investigation into Language Bias of Pairwise LLM-as-a-Judge

Xiaolin Zhou, Zheng Luo, Yicheng Gao +4

Recent advances in Large Language Models (LLMs) have incentivized the development of LLM-as-a-judge, an application of LLMs where they are used as judges to decide the quality of a…