DEFT: Distribution-guided Efficient Fine-Tuning for Human Alignment (2024.findings-emnlp)
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| Challenge: | Experimental results show that the methods enhanced by DEFT outperform the original methods in both alignment capability and generalization ability, with significantly reduced training time. |
| Approach: | They propose a distribution-based alignment framework that integrates data filtering and distributional guidance to improve alignment efficiency and generalization ability. |
| Outcome: | The proposed framework outperforms existing methods in alignment capability and generalization ability with significantly reduced training time. |
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| Challenge: | Reinforcement learning with human feedback (RLHF) is widely employed to align large language models with user intent. |
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