Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning
<|reference_start|>Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning: The surge in Large Language Models (LLMs) has revolutionized natural language processing, but fine-tuning them for specific tasks often encounters challenges in balancing performance and preserving general instruction-following abilities. In this paper, we posit that the distribution gap between task datasets and the LLMs serves as the primary underlying cause. To address the problem, we introduce Self-Distillation Fine-Tuning (SDFT), a novel approach that bridges the distribution gap by guiding fine-tuning with a distilled dataset generated by the model itself to match its original distribution. Experimental results on the Llama-2-chat model across various benchmarks demonstrate that SDFT effectively mitigates catastrophic forgetting while achieving comparable or superior performance on downstream tasks compared to the vanilla fine-tuning. Moreover, SDFT demonstrates the potential to maintain the helpfulness and safety alignment of LLMs. Our code is available at https://github.com/sail-sg/sdft.<|reference_end|>
@article{yang2024self-distillation,
    title={Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning},
    author={Zhaorui Yang, Tianyu Pang, Haozhe Feng, Han Wang, Wei Chen, Minfeng
  Zhu, Qian Liu},
    journal={arXiv preprint arXiv:2402.13669},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13669},
    primaryClass={cs.CL}
}