Summary of Re-adapt: Reverse Engineered Adaptation Of Large Language Models, by William Fleshman and Benjamin Van Durme
RE-Adapt: Reverse Engineered Adaptation of Large Language Models
by William Fleshman, Benjamin Van Durme
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We present RE-Adapt, a novel approach for adapting large language models to new domains without sacrificing pre-existing instruction-tuning knowledge. This method employs an adapter that isolates the learned instructions from the corresponding pretrained base model. Crucially, this process requires no additional data or training. By fine-tuning the base model on a new domain and then readapting it with the reverse-engineered adapter, we can achieve better performance compared to other methods across various popular large language models (LLMs) and datasets. Our results also demonstrate the effectiveness of RE-Adapt when used in conjunction with retrieval-augmented generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a super-smart AI that can understand and follow instructions, but it’s stuck learning one way of doing things. We created a way to help this AI learn new ways of doing things without forgetting what it already knows. This is called RE-Adapt. It works by taking the things the AI learned initially and using those as a starting point for learning something new. We tested RE-Adapt on several popular AI models and datasets, and it did better than other methods in many cases. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning » Retrieval augmented generation
