Summary of Towards Robust Alignment Of Language Models: Distributionally Robustifying Direct Preference Optimization, by Junkang Wu et al.
Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimization
by Junkang Wu, Yuexiang Xie, Zhengyi Yang, Jiancan Wu, Jiawei Chen, Jinyang Gao, Bolin Ding, Xiang Wang, Xiangnan He
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 study addresses the challenge of noise in training datasets for Direct Preference Optimization (DPO), a method for aligning Large Language Models (LLMs) with human preferences. The authors categorize noise into pointwise and pairwise types, which affect preference rankings. They utilize Distributionally Robust Optimization (DRO) to enhance DPO’s resilience to these types of noise. Theoretical insights reveal that DPO inherently embeds DRO principles, conferring robustness to pointwise noise with the regularization coefficient β playing a critical role in its noise resistance. The authors introduce Distributionally Robustifying DPO (Dr. DPO), which integrates pairwise robustness by optimizing against worst-case pairwise scenarios. Empirical evaluations demonstrate that Dr. DPO substantially improves text generation quality and response accuracy in preference datasets, showcasing enhanced performance in both noisy and noise-free settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps make language models better at understanding what humans like or dislike. Right now, these models can be tricked by fake data, which makes them not very good at generating text that people will enjoy. The researchers found a way to fix this problem by using a special technique called Distributionally Robust Optimization (DRO). They showed that this method makes the language model more resistant to fake data and helps it generate better text. |
Keywords
» Artificial intelligence » Language model » Optimization » Regularization » Text generation
