@inproceedings{ding-etal-2025-3ds,
    title = "3{DS}: Medical Domain Adaptation of {LLM}s via Decomposed Difficulty-based Data Selection",
    author = "Ding, Hongxin  and
      Fang, Yue  and
      Zhu, Runchuan  and
      Jiang, Xinke  and
      Zhang, Jinyang  and
      Xu, Yongxin  and
      Liao, Weibin  and
      Chu, Xu  and
      Zhao, Junfeng  and
      Wang, Yasha",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.983/",
    pages = "19473--19495",
    ISBN = "979-8-89176-332-6",
    abstract = "Large Language Models (LLMs) excel in general language tasks, motivating their adaptation to specialized domains such as healthcare. Effective domain adaptation typically involves supervised fine-tuning (SFT) on carefully selected instruction-tuning data. Current data selection methods adopt a data-centric approach, relying on external annotations and heuristics to identify externally defined high-quality or challenging data. Our exploratory experiments highlight this approach fails to improve the model{'}s domain performance, due to misalignment between selected data and the model{'}s knowledge distribution. To tackle this, we propose Decomposed Difficulty-based Data Selection (3DS), a two-stage model-centric data selection framework that aligns data selection with the model{'}s distribution. 3DS employs Prompt-Driven Data Selection to filter out noise based on the model{'}s knowledge via explicit alignment in Stage{\#}1, then adopts Decomposed Difficulty-based Data Selection to guide selection via three novel data difficulty metrics, including Instruction Understanding, Response Confidence, and Response Correctness in Stage{\#}2, enhanced by an attention-based importance weighting mechanism for accurate calibration.Extensive experiments in the healthcare domain show 3DS outperforms existing methods by up to 2.97{\%} accuracy, with additional validation in law and general domains, confirming its generalization ability. Our dataset and code are open-sourced at https://github.com/PuppyKnightUniversity/3DS."
}