Summary of Rosa: Random Subspace Adaptation For Efficient Fine-tuning, by Marawan Gamal Abdel Hameed et al.
ROSA: Random Subspace Adaptation for Efficient Fine-Tuning
by Marawan Gamal Abdel Hameed, Aristides Milios, Siva Reddy, Guillaume Rabusseau
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 The proposed Random Subspace Adaptation (ROSA) method outperforms existing parameter-efficient fine-tuning (PEFT) methods in adapting large models to downstream tasks, while maintaining zero latency overhead during inference time. ROSA achieves this by adapting subspaces of arbitrarily large dimension, making it strictly more expressive than LoRA without consuming additional memory during runtime. The authors demonstrate the effectiveness of ROSA in natural language processing (NLP) scenarios, including natural language generation (NLG) and natural language understanding (NLU), outperforming LoRA on almost every GLUE task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ROSA is a new way to adapt big models for tasks like writing or understanding language. It’s better than other methods because it can work with very large spaces of information, which makes it more powerful. ROSA doesn’t slow down the computer when it’s used, and it works well on language processing tasks. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Language understanding » Lora » Natural language processing » Nlp » Parameter efficient
