Summary of Direct Preference Optimization with Unobserved Preference Heterogeneity, by Keertana Chidambaram et al.
Direct Preference Optimization With Unobserved Preference Heterogeneity
by Keertana Chidambaram, Karthik Vinay Seetharaman, Vasilis Syrgkanis
First submitted to arxiv on: 23 May 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 This paper proposes a novel approach to aligning language models with varied human preferences, addressing the limitation of uniform preferences in existing methods like RLHF and DPO. The authors introduce an Expectation-Maximization adaptation to DPO, generating a mixture of models based on latent preference types of annotators. To produce a single generative method, they propose a min-max regret ensemble learning model that minimizes worst-case regret among subgroups with similar latent factors. This approach leverages the simplicity of DPO while accommodating diverse preferences. Experimental results demonstrate the effectiveness of this approach in producing equitable generative policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create better language models by considering different human opinions. Right now, most methods assume that everyone likes or dislikes things in the same way, but people are actually very different. The authors came up with a new idea to make language models more fair and considerate of these differences. They did this by mixing together different models based on what different people like, and then used an algorithm to find a single model that is good for everyone. This approach works well in practice and can help us create better language models. |
Keywords
» Artificial intelligence » Rlhf
