Yinuo Zhu | Searcharxiv

arXiv2026

BioDisclose: An Actionability-Aware Benchmark for Biomedical Safety under Adversarial Elicitation

Large language models (LLMs) increasingly support biomedical research, yet their behavior under adversarial requests for dual-use knowledge remains insufficiently characterized. We introduce BioDisclose, a benchmark for measuring biomedical knowledge disclosure under adversarial elicitation. BioDisclose contains 480 prompts derived from 24 expert-authored scenarios across six biomedical risk domains and four elicitation families spanning academic, historical, role-playing, and decomposed prompting. We grade model responses on a four-level scale from refusal to executable disclosure, distinguishing high-level discussion from technically specific and actionable content, including refuse-then-leak behavior. Across five deployed LLM systems, detailed-or-higher disclosure rates vary substantially, ranging from 9.2% to 64.0%. Academic framing is the most effective elicitation family on average (43.2%), while laboratory safety scenarios show the highest disclosure rate across domains (51.5%). These results reveal pronounced variation across models, prompting strategies, and biomedical risk categories, suggesting that current safeguards remain uneven in high-stakes scientific settings. BioDisclose provides a focused testbed for evaluating biomedical safety beyond binary refusal metrics.

cs.HC↗

arXiv2024

Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales

Traditional discriminative approaches in mental health analysis are known for their strong capacity but lack interpretability and demand large-scale annotated data. The generative approaches, such as those based on large language models (LLMs), have the potential to get rid of heavy annotations and provide explanations but their capabilities still fall short compared to discriminative approaches, and their explanations may be unreliable due to the fact that the generation of explanation is a black-box process. Inspired by the psychological assessment practice of using scales to evaluate mental states, our method which is called Mental Analysis by Incorporating Mental Scales (MAIMS), incorporates two procedures via LLMs. First, the patient completes mental scales, and second, the psychologist interprets the collected information from the mental scales and makes informed decisions. Experimental results show that MAIMS outperforms other zero-shot methods. MAIMS can generate more rigorous explanation based on the outputs of mental scales

cs.CL↗