Summary of Teamlora: Boosting Low-rank Adaptation with Expert Collaboration and Competition, by Tianwei Lin et al.
TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition
by Tianwei Lin, Jiang Liu, Wenqiao Zhang, Zhaocheng Li, Yang Dai, Haoyuan Li, Zhelun Yu, Wanggui He, Juncheng Li, Hao Jiang, Siliang Tang, Yueting Zhuang
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 addresses the limitations of Parameter-Efficient Fine-Tuning (PEFT) methods, particularly LoRA, in multidimensional task scenarios. Existing PEFT methods like LoRA have effectively addressed GPU memory constraints but often fall short in performance. To improve this, the authors introduce an innovative method called TeamLoRA that combines collaboration and competition between experts to enhance multi-task learning capabilities. The team-based approach consists of a knowledge-sharing mechanism for collaborative learning and a game-theoretic interaction for competitive transfer of domain-specific knowledge. This enables faster and more accurate PEFT paradigms for multi-task learning. To validate the superiority of TeamLoRA, the authors curate a comprehensive multi-task evaluation (CME) benchmark and conduct experiments on CME and other benchmarks, demonstrating the effectiveness and efficiency of TeamLoRA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning models work better together to learn many tasks at once. Currently, methods like LoRA are good but have limitations when doing many different tasks. The authors came up with a new idea called TeamLoRA that lets different “experts” work together and compete to improve their performance. This helps them learn faster and more accurately for many different tasks. To test this, the authors created a special benchmark to see how well TeamLoRA does compared to other methods. Their experiments showed that TeamLoRA is better at doing many tasks than other methods. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Machine learning » Multi task » Parameter efficient
