most citedEnhancing Interpretable Image Classification Through LLM Agents and Conditional Concept Bottleneck Models

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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

Dementia-Agents: A Multi-Modal Multi-Agent System for Dementia Staging and Phenotyping

Yaling Shen, Maja Christensen, Yiwen Jiang +4

Dementia diagnosis requires integrating multi-modal clinical assessments from diverse informants and clinicians under incomplete and heterogeneous data conditions. Yet most AI-driv…

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

CHiRPE: A Step Towards Real-World Clinical NLP with Clinician-Oriented Model Explanations

Stephanie Fong, Zimu Wang, Guilherme C. Oliveira +9

The medical adoption of NLP tools requires interpretability by end users, yet traditional explainable AI (XAI) methods are misaligned with clinical reasoning and lack clinician inp…

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

Enhancing Interpretable Image Classification Through LLM Agents and Conditional Concept Bottleneck Models

Yiwen Jiang, Deval Mehta, Wei Feng +1

Concept Bottleneck Models (CBMs) decompose image classification into a process governed by interpretable, human-readable concepts. Recent advances in CBMs have used Large Language…