Papers with accurate calibration
3DS: Medical Domain Adaptation of LLMs via Decomposed Difficulty-based Data Selection (2025.emnlp-main)
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Hongxin Ding, Yue Fang, Runchuan Zhu, Xinke Jiang, Jinyang Zhang, Yongxin Xu, Weibin Liao, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Effective domain adaptation typically involves supervised fine-tuning on carefully selected instruction-tuned data. |
| Approach: | They propose a model-centric data selection framework that aligns data selection with the model’s knowledge distribution to improve model performance. |
| Outcome: | The proposed framework outperforms existing methods by up to 2.97% accuracy in the healthcare domain. |