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: 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.

Source: http://arxiv.org/abs/2501.17860v1

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