7 papers · 2 filters
Characterizing Treatment-Context Medication Evidence Across Clinic Notes and Structured EHR Medication History
Mingyang Jiang, Congning Ni, Weixin Liu +1
Clinic notes and structured electronic health record (EHR) medication history often contain different medication information. Same-visit disagreement between these sources may resu…
CoRA: Confidence-Rationale Alignment for Reliable Chain-of-Thought Reasoning
Juming Xiong, Weixin Liu, Kevin Guo +9
Chain-of-thought (CoT) reasoning can improve LLM performance, but high answer confidence may be misleading when the accompanying CoT rationale is plausible yet incomplete or poorly…
Vectors Are Not Neutral: Sensitive-Information Inference from Exported LLM Representations in Summarization
Weixin Liu, Bowen Qu, Juming Xiong +3
Large language model (LLM) summarization systems may pass compact vector representations of private inputs to downstream retrieval, monitoring, audit, or analytic workflows. Even w…
MHGraphBench: Knowledge Graph-Grounded Benchmarking of Mental Health Knowledge in Large Language Models
Weixin Liu, Congning Ni, Shelagh A. Mulvaney +4
Large language models (LLMs) are increasingly used in the mental health domain, yet it remains unclear how well they capture related biomedical knowledge and how reliably they appl…
Disentangling Prompt Element Level Risk Factors for Hallucinations and Omissions in Mental Health LLM Responses
Congning Ni, Sarvech Qadir, Bryan Steitz +14
Mental health concerns are often expressed outside clinical settings, including in high-distress help seeking, where safety-critical guidance may be needed. Consumer health informa…
Coverage-Controlled Preference Mining from Noisy Claim Verification for Evidence-Grounded Generation
Weixin Liu, Congning Ni, Qingyuan Song +4
Evidence-grounded generation produces summaries whose claims should be supported by supplied evidence, but claim-level verifiers provide noisy feedback and can reward models that s…
Learning When to Sample: Confidence-Aware Selective Sampling for Efficient Chain-of-Thought Reasoning
Juming Xiong, Kevin Guo, Congning Ni +7
Large language models (LLMs) can achieve strong reasoning performance through chain-of-thought (CoT) reasoning, yet they often generate unnecessarily long reasoning paths that incu…