Papers with multi-stage fine-tuning baselines
Not All Parameters Are Created Equal: Smart Isolation Boosts Fine-Tuning Performance (2025.emnlp-main)
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| Challenge: | Extensive experiments demonstrate that our approach significantly alleviates task interference and forgetting. |
| Approach: | They propose a framework for supervised fine-tuning for large language models . they first fine-tail the model on each task to identify its core parameter regions . |
| Outcome: | The proposed framework outperforms vanilla fine-tuning and baselines on multiple public benchmarks on reasoning, dialogue, instruction following, and more. |