Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning

• URL: http://arxiv.org/abs/2402.13669v2
• Date: Tue, 28 May 2024 06:39:17 GMT
• Title: Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning
• Authors: Zhaorui Yang, Tianyu Pang, Haozhe Feng, Han Wang, Wei Chen, Minfeng Zhu, Qian Liu,
• Abstract summary: Self-Distillation Fine-Tuning (SDFT) is 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.
• Score: 29.72531930169291
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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