Papers with face Catastrophic Forgetting
Mitigating Catastrophic Forgetting in Large Language Models with Forgetting-aware Pruning (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) have shown impressive capabilities in various downstream tasks but typically face Catastrophic Forgetting (CF) during fine-tuning. |
| Approach: | They propose a pruning-based approach to balance CF and downstream task performance by integrating the ratio of the task vector to pre-trained model parameters into the pruning criteria. |
| Outcome: | The proposed pruning-based approach limits CF to just 0.25% while maintaining 99.67% accuracy on downstream tasks. |