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    <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.</abstract>
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%0 Conference Proceedings
%T Format Inertia: A Failure Mechanism of LLMs in Medical Pre-Consultation
%A Lim, Seungseop
%A Kim, Gibaeg
%A Han, Wooseok
%A Seo, Jean
%A Lee, Hyunkyung
%A Yoo, Jaehyo
%A Yang, Eunho
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F lim-etal-2025-format
%X 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.
%U https://aclanthology.org/2025.emnlp-industry.101/
%P 1437-1450

Markdown (Informal)

[Format Inertia: A Failure Mechanism of LLMs in Medical Pre-Consultation](https://aclanthology.org/2025.emnlp-industry.101/) (Lim et al., EMNLP 2025)

• Format Inertia: A Failure Mechanism of LLMs in Medical Pre-Consultation (Lim et al., EMNLP 2025)

ACL

• Seungseop Lim, Gibaeg Kim, Wooseok Han, Jean Seo, Hyunkyung Lee, Jaehyo Yoo, and Eunho Yang. 2025. Format Inertia: A Failure Mechanism of LLMs in Medical Pre-Consultation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1437–1450, Suzhou (China). Association for Computational Linguistics.