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Mitigating Catastrophic Forgetting in Large Language Models

Large language models face challenges with catastrophic forgetting and loss of plasticity when updated for specific tasks. Traditional learning methods force models to absorb task-specific information, leading to performance degradation on other tasks. There is a need for more adaptive learning approaches that balance task-specific adaptation with preservation of general reasoning abilities.

Computing & Technology, Computer Science, Machine Learning