Format Inertia: A Failure Mechanism of LLMs in Medical Pre-Consultation

• URL: http://arxiv.org/abs/2510.01688v2
• Date: Sat, 04 Oct 2025 10:16:08 GMT
• Title: Format Inertia: A Failure Mechanism of LLMs in Medical Pre-Consultation
• Authors: Seungseop Lim, Gibaeg Kim, Wooseok Han, Jean Seo, Hyunkyung Lee, Jaehyo Yoo, Eunho Yang,
• Abstract summary: We propose a data-centric method to mitigate Format Inertia in medical pre-consultation.<n> Experimental results show that our approach substantially alleviates Format Inertia in medical pre-consultation.
• Score: 33.11853966969629
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Large Language Models (LLMs) have brought significant improvements to various service domains, including chatbots and medical pre-consultation applications. In the healthcare domain, the most common approach for adapting LLMs to multi-turn dialogue generation is Supervised Fine-Tuning (SFT). However, datasets for SFT in tasks like medical pre-consultation typically exhibit a skewed turn-count distribution. Training on such data induces a novel failure mechanism we term Format Inertia, where models tend to generate repetitive, format-correct, but diagnostically uninformative questions in long medical dialogues. To mitigate this observed failure mechanism, we adopt a simple, data-centric method that rebalances the turn-count distribution of the training dataset. Experimental results show that our approach substantially alleviates Format Inertia in medical pre-consultation.

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