Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversations

• URL: http://arxiv.org/abs/2501.17860v1
• Date: Wed, 29 Jan 2025 18:58:48 GMT
• Title: Dialogue is Better Than Monologue: Instructing Medical LLMs via Strategical Conversations
• Authors: Zijie Liu, Xinyu Zhao, Jie Peng, Zhuangdi Zhu, Qingyu Chen, Xia Hu, Tianlong Chen,
• Abstract summary: We introduce a novel benchmark that simulates real-world diagnostic scenarios, integrating noise and difficulty levels aligned with USMLE standards.<n>We also explore dialogue-based fine-tuning, which transforms static datasets into conversational formats to better capture iterative reasoning processes.<n>Experiments show that dialogue-tuned models outperform traditional methods, with improvements of $9.64%$ in multi-round reasoning scenarios and $6.18%$ in accuracy in a noisy environment.
• Score: 74.83732294523402
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current medical AI systems often fail to replicate real-world clinical reasoning, as they are predominantly trained and evaluated on static text and question-answer tasks. These tuning methods and benchmarks overlook critical aspects like evidence-based reasoning and handling distracting information. To bridge this gap, we introduce a novel benchmark that simulates real-world diagnostic scenarios, integrating noise and difficulty levels aligned with USMLE standards. Moreover, we explore dialogue-based fine-tuning, which transforms static datasets into conversational formats to better capture iterative reasoning processes. Experiments show that dialogue-tuned models outperform traditional methods, with improvements of $9.64\%$ in multi-round reasoning scenarios and $6.18\%$ in accuracy in a noisy environment. Our findings highlight dialogue tuning as a promising approach for advancing clinically aligned and robust medical AI systems.

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