arXiv：cs.CL· Vishalakshi Arumugam, Dan Schumacher, Veronica Rammouz, Erfan Nourbakhsh, Enrique Gonzalez Guerrero, Jeremy Davis, Anthony Rios·· 4 小时前AI 评分34
用大语言模型理解临床认知对话
Understanding Clinical Cognitive Dialogues Using Large Language Models
AI 导读
研究者发布了一个去标识化的认知评估对话语料库，包含 33 段对话、8,250 条话语，标注了 3 种说话人角色和 56 种对话行为，并据此对 LLM 进行细粒度对话行为分类和下一句患者话语生成基准测试。
正文
Abstract:In-person cognitive assessment is both a test and an interaction. Clinicians explain tasks, repair misunderstandings, and adapt to patient responses, while patients may hesitate, seek clarification, or disengage. Yet clinical dialogue resources rarely label the interaction structure needed to study these behaviors at scale. We present an de-identified corpus of 33 cognitive assessment conversations with 8,250 utterances annotated for three speaker roles and 56 dialogue acts. We use this corpus to benchmark large language models on fine-grained dialogue-act classification and next-patient-utterance generation. We also test whether out-of-domain instruction data and explanation-augmented training transfer to this clinical setting. Instruction tuning produces the strongest patient-utterance reference matching and improves classification accuracy. Reasoning-aware fine-tuning produces the strongest classification results among the LLaMA-3.1-8B variants. However, even the best models struggle to separate closely related dialogue acts, showing that broad conversational intent is easier to recognize than fine-grained communicative function. The corpus and benchmark make interaction structure measurable in cognitive assessments and support follow-up work on conversational markers, clinician education, and carefully validated simulated patients. This work does not make diagnostic claims. Instead, it provides the data and evaluation framework needed to study these applications.
| Comments: | 9 pages |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.34125 [cs.CL] |
| (or arXiv:2609.34125v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.34125 arXiv-issued DOI via DataCite |
Submission history
From: Anthony Rios [view email]
[v1]
Mon, 28 Sep 2026 02:04:25 UTC (447 KB)
[v2]
Thu, 1 Oct 2026 21:18:18 UTC (447 KB)
来源：arXiv：cs.CL · arxiv.org