arXiv cs.CL· Daniel P. Jeong, Yewon Byun, John Giorgi, Peng Xu, Zachary C. Lipton, Michael Oberst·· 10 小时前AI 评分31
从用户编辑数据中筛选可操作反馈，提升临床文档生成模型能力
Actionable Feedback: Improving Model Capabilities from User Edit Data on Clinical Documentation
AI 导读
针对临床文档场景中超过 45% 的编辑输出包含输入不支持的用户新增信息，研究提出按临床医生编辑行为分类生成半合成编辑数据，并用简单过滤方法识别可操作编辑。在基于 MIMIC-III 的出院小结生成任务上，直接对原始编辑输出微调会增加无依据内容，而逐步过滤后微调可在保持对输入上下文忠实度的同时提升临床医生对齐指标。
正文 · 原文
Abstract:Large language models (LLMs) are increasingly being deployed in clinical documentation workflows, where clinicians review and edit their outputs before use. In principle, these clinician edits represent a scalable source of supervision for model training. In practice, not all edits represent actionable feedback: for instance, on data drawn from real-world deployment of an AI clinical documentation product, our review suggests that over 45% of edited outputs contain user-added information that is not directly supported by the inputs to the model. Naively fine-tuning on such edits risks teaching the model to hallucinate. In this paper, we develop a method to address this challenge. Given that real clinician edits on deployed documentation tools are not publicly available, we first develop a taxonomy of clinician-editing behaviors, informed by data from a deployed AI clinical documentation product. Then, we develop a method for generating semi-synthetic clinician-edit data, informed by our taxonomy, on a discharge-summary generation task based on MIMIC-III, a public dataset. Finally, we introduce simple filtering methods for identifying actionable edits, with accuracy evaluated by expert review. We show empirically, on our semi-synthetic dataset, that naive fine-tuning on raw edited output increases ungrounded content, while our approach of progressively filtering toward grounded edits enables improvement on clinician-alignment metrics while retaining faithfulness to the input context.
| Comments: | Preprint |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.12904 [cs.CL] |
| (or arXiv:2611.12904v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.12904 arXiv-issued DOI via DataCite |
Submission history
From: Daniel Jeong [view email]
[v1]
Wed, 14 Oct 2026 19:54:40 UTC (656 KB)
来源：arXiv cs.CL · arxiv.org