使用基于 LLM 和人类对齐的指标评估医学报告中的因果解释
Evaluating Causal Explanation in Medical Reports with LLM-Based and Human-Aligned Metrics
AI总结：
本研究比较了六种指标在评估自动生成诊断报告中因果解释质量的表现，发现 GPT-Black 和 GPT-White 与专家评估高度一致，优于相似度指标，支持基于 LLM 的评估用于因果推理任务。
摘要：
本研究调查了不同评估指标在多大程度上能准确捕捉自动生成的诊断报告中因果解释的质量。我们比较了六种指标：BERTScore、Cosine Similarity、BioSentVec、GPT-White、GPT-Black 和专家定性评估，涵盖两种输入类型：基于观察和基于多选题的报告生成。应用了两种加权策略：一种反映任务特定优先级，另一种对所有指标分配相等权重。结果表明，GPT-Black 在识别逻辑连贯且临床有效的因果叙述方面表现出最强的区分能力。GPT-White 也与专家评估高度一致，而基于相似度的指标则偏离临床推理质量。这些发现强调了指标选择和加权对评估结果的影响，支持在需要可解释性和因果推理的任务中使用基于 LLM 的评估。
英文摘要：
This study investigates how accurately different evaluation metrics capture the quality of causal explanations in automatically generated diagnostic reports. We compare six metrics: BERTScore, Cosine Similarity, BioSentVec, GPT-White, GPT-Black, and expert qualitative assessment across two input types: observation-based and multiple-choice-based report generation. Two weighting strategies are applied: one reflecting task-specific priorities, and the other assigning equal weights to all metrics. Our results show that GPT-Black demonstrates the strongest discriminative power in identifying logically coherent and clinically valid causal narratives. GPT-White also aligns well with expert evaluations, while similarity-based metrics diverge from clinical reasoning quality. These findings emphasize the impact of metric selection and weighting on evaluation outcomes, supporting the use of LLM-based evaluation for tasks requiring interpretability and causal reasoning.
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。
