Summary of Reward-augmented Data Enhances Direct Preference Alignment Of Llms, by Shenao Zhang et al.
Reward-Augmented Data Enhances Direct Preference Alignment of LLMs
by Shenao Zhang, Zhihan Liu, Boyi Liu, Yufeng Zhang, Yingxiang Yang, Yongfei Liu, Liyu Chen, Tao Sun, Zhaoran Wang
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The abstract presents a study that aims to improve the performance of Large Language Models (LLMs) in following human instructions and intentions. The existing direct alignment algorithms focus on relative preferences but often overlook qualitative aspects of responses, which can lead to overfitting and unlearning of high-quality rejected responses. To address this issue, the researchers introduce reward-conditioned LLM policies that learn from the entire spectrum of response quality within a dataset. They propose a simple data relabeling method that conditions preference pairs on quality scores to construct a reward-augmented dataset. This approach is shown to consistently boost the performance of DPO models across diverse models and improves average accuracy on various academic benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study aims to help Large Language Models follow human instructions better by introducing a new approach to learning from preferences. Existing methods focus on which responses are preferred, but this can lead to overfitting and forgetting good responses. The researchers propose a new way of learning that takes into account the quality of all responses, not just the best ones. They also suggest a simple way to add more information to datasets to help models learn better. This approach is shown to work well across different models and tasks. |
Keywords
» Artificial intelligence » Alignment » Overfitting
