Summary of Rosa: Accurate Parameter-efficient Fine-tuning Via Robust Adaptation, by Mahdi Nikdan et al.
RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation
by Mahdi Nikdan, Soroush Tabesh, Elvir Crnčević, Dan Alistarh
First submitted to arxiv on: 9 Jan 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 Robust Adaptation (RoSA), a novel parameter-efficient fine-tuning (PEFT) method that leverages robust principal component analysis to jointly train low-rank and highly-sparse components on top of fixed pretrained weights. RoSA efficiently approximates the performance of full-fine-tuning solutions while reducing computational and memory budgets. Across generative tasks such as math and SQL query generation, RoSA outperforms LoRA, pure sparse fine-tuning, and alternative hybrid methods at the same parameter budget. The method also recovers FFT performance on some tasks. Our code is available at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re working on a new way to make language models better. This new approach is called Robust Adaptation (RoSA). It helps us get good results even when we have limited computer power and memory. We tested RoSA on some tricky math problems and generating SQL queries, and it did really well! In fact, it was almost as good as the most powerful way of fine-tuning language models. Our code is available online if you want to try it out. |
Keywords
* Artificial intelligence * Fine tuning * Lora * Parameter efficient * Principal component analysis
