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RE-Adapt: Reverse Engineered Adaptation of Large Language Models
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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.
Forward citations
Cited by 2 Pith papers
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Adapt Once, Thrive with Updates: Transferable Parameter-Efficient Fine-Tuning on Evolving Base Models
Trans-PEFT uses random FFN masking and layer dropping during fine-tuning so PEFT modules trained on an old base model transfer to a continually updated base model without retuning.
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LoRA-Augmented Generation (LAG) for Knowledge-Intensive Language Tasks
LAG is a two-stage router that filters a 1,000-adapter LoRA library with Arrow and reranks with SpectR, outperforming the Arrow baseline and reaching 92.1% of its Oracle's performance on KILT tasks.
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