aiqbench analysis
MedTextWeaver addresses the challenge of adapting large language models in clinical settings by converting sparse, fragmented expert feedback into coherent procedural knowledge without requiring additional training data. This approach significantly improves LLM performance on medical text editing tasks while offering an interpretable interface that bridges raw evaluations with model behavior. The work advances agentic AI capabilities specifically within high-stakes healthcare domains where supervision is inherently limited.
MedTextWeaver: Procedural Knowledge Evolution in Agentic Medical Text Editing
arXiv:2602.00740v2 Announce Type: replace-cross Abstract: Medical text editing is essential for improving communication among diverse stakeholders in clinical settings. However, adapting LLM agents to this task remains challenging because expert supervision is often sparse, fragmented, and distributed across interacting quality dimensions. We identify that direct accumulation or retrieval of individual feedback is insufficient for effective adaptation, as fragmented evaluations do not directly translate into a coherent understanding of medical text quality. Based on this observation, we propose MedTextWeaver, a training-free framework that transforms fragmented evaluative evidence into global quality principles and actionable procedural knowledge for medical text editing. Across three clinical text datasets and a real-world validation experiment, MedTextWeaver consistently improves performance over strong LLM baselines and existing memory-based adaptation approaches. Further analysis demonstrates that the learned knowledge enables more effective adaptation under limited supervision while providing an explicit and interpretable interface between expert evaluations and LLM editing behavior.
Abstract:Medical text editing is essential for improving communication among diverse stakeholders in clinical settings. However, adapting LLM agents to this task remains challenging because expert supervision is often sparse, fragmented, and distributed across interacting quality dimensions. We identify that direct accumulation or retrieval of individual feedback is insufficient for effective adaptation, as fragmented evaluations do not directly translate into a coherent understanding of medical text quality. Based on this observation, we propose MedTextWeaver, a training-free framework that transforms fragmented evaluative evidence into global quality principles and actionable procedural knowledge for medical text editing. Across three clinical text datasets and a real-world validation experiment, MedTextWeaver consistently improves performance over strong LLM baselines and existing memory-based adaptation approaches. Further analysis demonstrates that the learned knowledge enables more effective adaptation under limited supervision while providing an explicit and interpretable interface between expert evaluations and LLM editing behavior.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2602.00740 [cs.CL]
(or arXiv:2602.00740v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2602.00740 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 240 words
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