Summary of Rewardbench: Evaluating Reward Models For Language Modeling, by Nathan Lambert et al.
RewardBench: Evaluating Reward Models for Language Modeling
by Nathan Lambert, Valentina Pyatkin, Jacob Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi
First submitted to arxiv on: 20 Mar 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 A novel approach to evaluate reward models in reinforcement learning-based human feedback (RLHF) is presented. Reward models are crucial for aligning pre-trained language models with human preferences. However, their evaluation has been relatively understudied. The proposed RewardBench dataset and code-base aim to enhance scientific understanding of reward models by providing a benchmark for evaluating their performance on challenging queries. The dataset includes prompt-chosen-rejected trios spanning chat, reasoning, and safety tasks. Comparison datasets are created to evaluate the propensity for refusals, reasoning limitations, and instruction following shortcomings of various reward models. The study trains multiple reward models using different methods, including direct MLE training and Direct Preference Optimization (DPO). The findings provide insights into the RLHF process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reward models are important because they help make language models align with what humans want. But nobody had really looked at how well these models do their job. To change that, scientists created a special set of examples called RewardBench. This helps us understand how different models perform when faced with tricky questions. The examples cover chat, reasoning, and safety topics. By testing many models on this dataset, we can learn more about what they’re good at and where they struggle. This research is important because it helps us understand how to make language models work better for us. |
Keywords
* Artificial intelligence * Optimization * Prompt * Reinforcement learning * Rlhf
