Summary of Beyond the Binary: Capturing Diverse Preferences with Reward Regularization, by Vishakh Padmakumar et al.
Beyond the Binary: Capturing Diverse Preferences With Reward Regularization
by Vishakh Padmakumar, Chuanyang Jin, Hannah Rose Kirk, He He
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 proposed paper addresses the limitations of current large language models (LLMs) in capturing diverse user preferences through public-facing interfaces. Existing approaches rely on binary judgment-based reward models, which neglect to capture the broader, aggregated preferences in real-world tasks. The authors identify two dimensions of subjectivity – Plurality of Responses to Prompts and Indistinguishability of Responses – where users disagree on preferred outputs. They show that current reward models weakly correlate with user preferences in these cases. To address this issue, the authors introduce a simple method to augment existing binary preference datasets with synthetic judgments, which is then incorporated as regularization during model training. This approach yields predictions that better align with aggregate user preferences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) help people interact with millions of users through public-facing interfaces. Right now, these models are trained using a specific way to choose the best response from pairs of possible answers. However, this method doesn’t capture how individual users might prefer different responses in real-life situations. The authors want to fix this by introducing two new dimensions: Plurality (where multiple correct answers are allowed) and Indistinguishability (where similar responses exist). They found that current methods don’t work well with these types of preferences. To improve this, they came up with a simple method to add more information to existing data sets, which helps train the models better. |
Keywords
» Artificial intelligence » Regularization
