RE-Adapt: Reverse Engineered Adaptation of Large Language Models
<|reference_start|>RE-Adapt: Reverse Engineered Adaptation of Large Language Models: 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.<|reference_end|>
@article{fleshman2024re-adapt:,
    title={RE-Adapt: Reverse Engineered Adaptation of Large Language Models},
    author={William Fleshman and Benjamin Van Durme},
    journal={arXiv preprint arXiv:2405.15007},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15007},
    primaryClass={cs.CL cs.AI cs.LG}
}