Summary of Rapid Switching and Multi-adapter Fusion Via Sparse High Rank Adapters, by Kartikeya Bhardwaj et al.
Rapid Switching and Multi-Adapter Fusion via Sparse High Rank Adapters
by Kartikeya Bhardwaj, Nilesh Prasad Pandey, Sweta Priyadarshi, Viswanath Ganapathy, Rafael Esteves, Shreya Kadambi, Shubhankar Borse, Paul Whatmough, Risheek Garrepalli, Mart Van Baalen, Harris Teague, Markus Nagel
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes Sparse High Rank Adapters (SHiRA), a technique for finetuning the weights of base models while maintaining sparsity. By adapting only 1-2% of the model’s parameters, SHiRA achieves rapid switching and reduced concept-loss during multi-adapter fusion. The authors demonstrate the effectiveness of SHiRA on Large Vocabulary Models (LVMs) and Large Language Models (LLMs), showing that finetuning a small portion of the base model is sufficient for many tasks and outperforms Low Rank Adaptation (LoRA). Additionally, SHiRA can be easily combined with existing techniques. The paper’s contributions include the development of SHiRA and its application to various tasks, showcasing its potential in improving language understanding and processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way to adapt computer models for natural language processing. They developed something called Sparse High Rank Adapters (SHiRA) that allows them to update only small parts of the model while keeping most of it unchanged. This makes the process faster and more efficient, which is useful when working with large amounts of data. The authors tested SHiRA on different types of models and found that it performed well, even better than some other methods. They hope this technology will help improve our understanding of language and make computers better at processing it. |
Keywords
» Artificial intelligence » Language understanding » Lora » Low rank adaptation » Natural language processing
