Summary of Pal: Pluralistic Alignment Framework For Learning From Heterogeneous Preferences, by Daiwei Chen et al.
PAL: Pluralistic Alignment Framework for Learning from Heterogeneous Preferences
by Daiwei Chen, Yi Chen, Aniket Rege, Ramya Korlakai Vinayak
First submitted to arxiv on: 12 Jun 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 Large-scale foundation models require additional alignment with human preferences to be deployable. Current methods rely on collecting pairwise comparisons from humans and learning a reward model or policy using the Bradley-Terry-Luce (BTL) model. However, these methods assume a universal preference shared by all humans, lacking flexibility in adapting to plurality of opinions and preferences. Our proposed framework, PAL, models human preference complementary to existing pretraining strategies, incorporating plurality from the ground up. We utilize the ideal point model as a lens for alignment using preference comparisons, reformulating the problem with mixture modeling. This approach captures the plurality of population preferences while learning a common preference latent space across different preferences, enabling few-shot generalization to new users. Our framework leverages large foundation models and simple MLP layers to learn reward functions comparable to state-of-the-art models, enhancing efficiency in reward modeling. We demonstrate PAL’s competitive accuracy on language model summaries, image generative models, and a novel semisynthetic heterogeneous dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making large AI models more useful for humans. Right now, these models are not ready to use because they don’t understand what people like or dislike. To fix this, researchers usually ask humans many questions like “Do you prefer A or B?” and then use that information to teach the model. But this approach has a big limitation: it assumes everyone likes the same things. Our team came up with a new way to solve this problem by allowing for different opinions and preferences. We developed a framework called PAL that can learn from people’s preferences and create a common understanding of what they like. This helps AI models become more helpful and efficient. |
Keywords
» Artificial intelligence » Alignment » Few shot » Generalization » Language model » Latent space » Pretraining
