activity
20242026
most citedA Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers

1 citations · 2 across the 17 of their papers we have counts for

collaborators
Showing cs.CLShow all

6 papers · 1 filter

cs.CL2026

SDARE-Bench: Evaluating Large Language Models on Conversational Stigma Detection and Response in Dyadic and Group Dialogue

Stephanie Fong, Yiwen Jiang, Zimu Wang +12

Large Language Models (LLMs) are increasingly used in advice seeking and decision making that may affect social judgements. Despite stigma's profound effects on people and communit…

cs.CL2026

AnchorSIPS: A Synthetic Dataset and Evaluation Resource for Evidence-Supported Psychosis-Risk Symptom Measurement

Guilherme C. Oliveira, Stephanie Fong, Zimu Wang +10

Progress on AI for psychosis-risk assessment is limited by a data-access bottleneck. Real clinical interviews are difficult to share because of privacy, governance, and consent con…

cs.CL2026

Do No Harm: Exposing Hidden Vulnerabilities of LLMs via Persona-based Client Simulation Attack in Psychological Counseling

Qingyang Xu, Yaling Shen, Stephanie Fong +7

The increasing use of large language models (LLMs) in mental healthcare raises safety concerns in high-stakes therapeutic interactions. A key challenge is distinguishing therapeuti…

cs.CL2026

PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics

Yaling Shen, Stephanie Fong, Yiwen Jiang +9

The increasing integration of large language models (LLMs) into mental health applications necessitates robust frameworks for evaluating professional safety alignment. Current eval…

cs.CL2025★ 1 cited

A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers

Ming Hu, Chenglong Ma, Wei Li +117

Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research, yet their progress is shaped by the compl…

cs.CL2025

MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation

Haochen Xue, Feilong Tang, Ming Hu +13

Recent multimodal large language models (MLLMs) have demonstrated significant potential in open-ended conversation, generating more accurate and personalized responses. However, th…