Summary of Is Parameter Collision Hindering Continual Learning in Llms?, by Shuo Yang and Kun-peng Ning and Yu-yang Liu and Jia-yu Yao and Yong-hong Tian and Yi-bing Song and Li Yuan
Is Parameter Collision Hindering Continual Learning in LLMs?
by Shuo Yang, Kun-Peng Ning, Yu-Yang Liu, Jia-Yu Yao, Yong-Hong Tian, Yi-Bing Song, Li Yuan
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper tackles the problem of catastrophic forgetting in Large Language Models (LLMs) when learning multiple tasks sequentially. The authors reveal that building non-collision parameters is a crucial factor in addressing continual learning (CL) challenges, rather than constructing orthogonality tasks as existing state-of-the-art methods do. Their theoretical and experimental analyses show that non-collision parameters provide better task orthogonality, allowing knowledge from multiple domains to be preserved in subspaces, making it harder to forget previously seen data. The authors propose Non-collision Low-Rank Adaptation (N-LoRA), a simple yet effective approach leveraging low collision rates to enhance CL in LLMs. Experimental results on multiple CL benchmarks demonstrate that N-LoRA achieves superior performance (+2.9%), higher task orthogonality (4.1 times), and lower parameter collision (58.1 times) compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps Large Language Models learn new things without forgetting what they already know. When we teach an LLM multiple tasks one after the other, it often forgets previous lessons. To solve this problem, researchers have been trying different approaches. This study shows that a key factor is making sure the model’s parameters don’t get mixed up with each other. The authors propose a new method called N-LoRA that helps LLMs remember what they’ve learned before while still learning new things. In experiments, N-LoRA performed better than existing methods in keeping knowledge from multiple domains. |
Keywords
» Artificial intelligence » Continual learning » Lora » Low rank adaptation
