提出N-LoRA，通过降低参数冲突提升大模型持续学习能力
Is Parameter Collision Hindering Continual Learning in LLMs?
- 设计低碰撞率的参数更新机制，增强任务间正交性
- 在多个基准上实现性能提升2.9分，参数冲突降低58.1倍
- 适合需要长期增量学习的大模型应用开发者
大型语言模型在顺序学习多个任务时常出现灾难性遗忘，持续学习（CL）成为其动态部署的关键。现有最先进方法如O-LoRA通常通过构建正交任务来解耦不同领域间的参数依赖。本文揭示，避免参数冲突才是解决CL挑战更关键的因素。理论与实验分析表明，非冲突参数能提供更好的任务正交性，是充分但非必要条件；多领域知识可保留在非冲突参数子空间中，更难遗忘已有数据。基于此，我们提出非冲突低秩适配（N-LoRA），一种简单有效的持续学习方法，利用低参数碰撞率提升大模型性能。在多个持续学习基准测试中，N-LoRA表现优于现有最优方法：性能提升2.9分，任务正交性提高4.1倍，参数冲突降低58.1倍。
原文摘要 · Abstract (English)
Large Language Models (LLMs) often suffer from catastrophic forgetting when learning multiple tasks sequentially, making continual learning (CL) essential for their dynamic deployment. Existing state-of-the-art (SOTA) methods, such as O-LoRA, typically focus on constructing orthogonality tasks to decouple parameter interdependence from various domains.In this paper, we reveal that building non-collision parameters is a more critical factor in addressing CL challenges. Our theoretical and experimental analyses demonstrate that non-collision parameters can provide better task orthogonality, which is a sufficient but unnecessary condition. Furthermore, knowledge from multiple domains will be preserved in non-collision parameter subspaces, making it more difficult to forget previously seen data. Leveraging this insight, we 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 indicate that N-LoRA achieves superior performance (+2.9), higher task orthogonality (*4.1 times), and lower parameter collision (*58.1 times) than SOTA methods.
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