Summary of Towards Comprehensive Preference Data Collection For Reward Modeling, by Yulan Hu et al.

Towards Comprehensive Preference Data Collection for Reward Modeling

by Yulan Hu, Qingyang Li, Sheng Ouyang, Ge Chen, Kaihui Chen, Lijun Mei, Xucheng Ye, Fuzheng Zhang, Yong Liu

First submitted to arxiv on: 24 Jun 2024

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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 reinforcement learning framework is introduced to enhance the alignment of large language models with human preferences. The approach, called Reinforcement Learning from Human Feedback (RLHF), relies on a reward model trained on preference data to guide the generation of high-quality responses. However, collecting this preference data remains an open problem. Existing methods rely on either AI or humans to identify chosen and rejected instances among pairwise responses. This process may not effectively filter out noise and ensure sufficient diversity in collected data. To address these concerns, a comprehensive framework for preference data collection is proposed, consisting of four incremental steps: Prompt Generation, Response Generation, Response Filtering, and Human Labeling. This structured approach ensures the collection of high-quality preferences while reducing reliance on human labor.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Large language models can generate responses that are more helpful if they’re aligned with what people want to hear. A key challenge is collecting data that shows which responses are good or bad. Some methods use AI to help, but others rely on humans. However, these approaches may not work well and might even introduce errors. To fix this, researchers have created a step-by-step plan for collecting preference data. This plan includes four parts: generating prompts, generating responses, filtering out bad responses, and asking people to label the good ones. By following this framework, we can collect high-quality data that helps language models generate better responses.