<ul><li>Large Language Models (LLMs) often suffer from catastrophic forgetting when learning multiple tasks sequentially, making continual learning (CL) essential for their dynamic deployment.</li><li>Existing state-of-the-art (SOTA) methods focus on constructing orthogonality tasks to decouple parameter interdependence from various domains.</li><li>However, this paper suggests that building non-collision parameters is a more critical factor in addressing CL challenges.</li><li>The proposed approach, Non-collision Low-Rank Adaptation (N-LoRA), leverages low collision rates to enhance CL in LLMs with superior performance, higher task orthogonality, and lower parameter collision than SOTA methods.</li></ul>

Is Parameter Collision Hindering Continual Learning in LLMs?

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