RE-Adapt: Reverse Engineered Adaptation of Large Language Models

William Fleshman; Benjamin Van Durme

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arXiv · 2405.15007

RE-Adapt: Reverse Engineered Adaptation of Large Language Models

William Fleshman·Benjamin Van Durme

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Abstract

We introduce RE-Adapt, an approach to fine-tuning large language models on new domains without degrading any pre-existing instruction-tuning. We reverse engineer an adapter which isolates what an instruction-tuned model has learned beyond its corresponding pretrained base model. Importantly, this requires no additional data or training. We can then fine-tune the base model on a new domain and readapt it to instruction following with the reverse engineered adapter. RE-Adapt and our low-rank variant LoRE-Adapt both outperform other methods of fine-tuning, across multiple popular LLMs and datasets, even when the models are used in conjunction with retrieval-augmented generation.

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BibTeX RIS

William Fleshman, Benjamin Van Durme. 2024-05-23. RE-Adapt: Reverse Engineered Adaptation of Large Language Models. https://arxiv.org/abs/2405.15007

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