RE-Adapt: Reverse Engineered Adaptation of Large Language Models
Keywords: Large Language Model, Fine-Tuning, Instruction-Tuning, Reverse Engineer, Adapter, LoRA, DoRA, QA, LLama-3, Gemma, Mistral
TL;DR: The difference in weights between an instruction-tuned and pretrained LLM can be used as an instruction-adapter, enabling efficient fine-tuning of the pretrained model before readapting it to instruction-following.
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.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 7143
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