RE-Adapt: Reverse Engineered Adaptation of Large Language Models
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916258822225920 |
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| author | Fleshman, William Van Durme, Benjamin |
| author_facet | Fleshman, William Van Durme, Benjamin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15007 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RE-Adapt: Reverse Engineered Adaptation of Large Language Models Fleshman, William Van Durme, Benjamin Computation and Language Artificial Intelligence Machine Learning 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. |
| title | RE-Adapt: Reverse Engineered Adaptation of Large Language Models |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.15007 |