Summary of On the Generalization Of Preference Learning with Dpo, by Shawn Im et al.
On the Generalization of Preference Learning with DPO
by Shawn Im, Yixuan Li
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel theoretical framework for analyzing generalization guarantees of large language models (LLMs) is proposed to ensure alignment with human values. The framework, based on direct preference optimization (DPO), rigorously assesses how well models generalize after a finite number of gradient steps, reflecting real-world LLM training practices. By bounding the generalization error using the reward margin associated with each sample and its trajectory throughout training, the paper derives learning guarantees showing that DPO-trained models can correctly discern preferred responses on unseen data with high probability. Empirical validation is provided on contemporary LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models struggle to align with human preferences, leading to harmful outputs. To fix this, preference learning trains models using human feedback. But we still don’t fully understand how well these models generalize. This paper helps fill that gap by creating a new theoretical framework for direct preference optimization (DPO). The framework shows how well models generalize after training. By analyzing the reward margin of each sample and its journey during training, we can bound the generalization error. The paper then shows that DPO-trained models can correctly identify preferred responses on new data with high probability. This is important because it helps us understand why some language models work better than others. |
Keywords
» Artificial intelligence » Alignment » Generalization » Optimization » Probability
