Summary of Vickreyfeedback: Cost-efficient Data Construction For Reinforcement Learning From Human Feedback, by Guoxi Zhang et al.
VickreyFeedback: Cost-efficient Data Construction for Reinforcement Learning from Human Feedback
by Guoxi Zhang, Jiuding Duan
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Science and Game Theory (cs.GT); General Economics (econ.GN)
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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 paper explores the economic efficiency of Reinforcement Learning from Human Feedback (RLHF), which uses human preference datasets to train large language models. The authors highlight the importance of considering the monetized cost of preference annotation, as existing algorithms struggle to capture comprehensive preferences due to complex relationships in these datasets. To address this issue, the paper introduces an auction mechanism to improve the efficiency of preference data collection, demonstrating that it can enhance cost-efficiency while maintaining model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RLHF uses human feedback to teach language models what’s good and bad. But collecting this feedback comes at a cost, which hasn’t been considered before. The problem is that existing methods for using this feedback are not very efficient, making it hard to get high-quality feedback. This paper proposes an auction system to help make the process more cost-effective, showing that it can work well while still producing good results. |
Keywords
* Artificial intelligence * Reinforcement learning from human feedback * Rlhf
