《不纯粹理性批判：揭示医疗大型语言模型的推理行为》
Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Models
AI总结：
本文针对医疗LLM推理行为研究不足的问题，梳理并分类相关建模与评估方法，提出理论框架以揭示模型底层推理操作，并指出大型推理模型的开放挑战。
摘要：
背景：尽管大型语言模型（LLM）目前已在医疗领域广泛普及，但令人惊讶的是，针对其推理行为的研究仍然匮乏。我们强调理解推理行为而非高层预测准确率的重要性，因为在此语境下，这等同于可解释人工智能（XAI）。特别是，在临床领域使用的医疗LLM中实现XAI，将对整个医疗保健行业产生重大影响。结果：因此，在本研究中，我们调整了现有的推理行为概念，并阐明其在医疗LLM特定情境下的解释。我们调研并分类了当前用于建模和评估医疗LLM推理的最先进方法。此外，我们提出了理论框架，使医疗专业人员或机器学习工程师能够洞察这些此前不透明模型的底层推理操作。我们还概述了大型推理模型发展面临的关键开放挑战。结论：随后临床医生和患者对医疗机器学习模型透明度和信任度的提升，将加速医疗AI在整个医疗保健系统中的整合、应用及进一步发展。
英文摘要：
Background: Despite the current ubiquity of Large Language Models (LLMs) across the medical domain, there is a surprising lack of studies which address their reasoning behaviour. We emphasise the importance of understanding reasoning behaviour as opposed to high-level prediction accuracies, since it is equivalent to explainable AI (XAI) in this context. In particular, achieving XAI in medical LLMs used in the clinical domain will have a significant impact across the healthcare sector. Results: Therefore, in this work, we adapt the existing concept of reasoning behaviour and articulate its interpretation within the specific context of medical LLMs. We survey and categorise current state-of-the-art approaches for modeling and evaluating reasoning reasoning in medical LLMs. Additionally, we propose theoretical frameworks which can empower medical professionals or machine learning engineers to gain insight into the low-level reasoning operations of these previously obscure models. We also outline key open challenges facing the development of Large Reasoning Models. Conclusion: The subsequent increased transparency and trust in medical machine learning models by clinicians as well as patients will accelerate the integration, application as well as further development of medical AI for the healthcare system as a whole.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
