Summary of Reinforcement Learning From Multi-role Debates As Feedback For Bias Mitigation in Llms, by Ruoxi Cheng et al.
Reinforcement Learning from Multi-role Debates as Feedback for Bias Mitigation in LLMs
by Ruoxi Cheng, Haoxuan Ma, Shuirong Cao, Jiaqi Li, Aihua Pei, Zhiqiang Wang, Pengliang Ji, Haoyu Wang, Jiaqi Huo
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper proposes a novel approach to bias mitigation in Large Language Models (LLMs), called Reinforcement Learning from Multi-role Debates as Feedback (RLDF). The authors find that involving LLMs in role-playing scenarios boosts their ability to recognize and mitigate biases. To achieve this, they utilize LLMs in multi-role debates to create a dataset with both high-bias and low-bias instances for training the reward model in reinforcement learning. The approach consists of two modes: self-reflection, where the same LLM participates in multi-role debates, and teacher-student, where a more advanced LLM like GPT-3.5-turbo guides the LLM to perform this task. Experimental results demonstrate the effectiveness of RLDF in bias mitigation across different LLMs on BBQ and custom datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making Large Language Models fairer by reducing their biases. Biases can make user experiences bad and affect society as a whole. Right now, we need to teach these models using lots of human feedback, which isn’t very effective or transferable to other topics. The authors found that if we get these models to engage in role-playing scenarios, they can learn to recognize and reduce biases on their own. They propose a new approach called RLDF, which replaces human feedback with multi-role debates. This helps the models learn from each other’s strengths and weaknesses. The results show that this approach works well for reducing biases across different models and datasets. |
Keywords
» Artificial intelligence » Gpt » Reinforcement learning
