Summary of A Multi-llm Debiasing Framework, by Deonna M. Owens et al.
A Multi-LLM Debiasing Framework
by Deonna M. Owens, Ryan A. Rossi, Sungchul Kim, Tong Yu, Franck Dernoncourt, Xiang Chen, Ruiyi Zhang, Jiuxiang Gu, Hanieh Deilamsalehy, Nedim Lipka
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 debiasing Large Language Models (LLMs) using a multi-LLM framework. Despite significant advancements in bias mitigation techniques, biases continue to persist in LLMs, including subtle biases that may elude human detection. The proposed framework consists of two distinct approaches: a centralized method, where a single central LLM facilitates the conversation, and a decentralized method, where all models communicate directly. Experimental results show that the multi-LLM framework significantly reduces bias in LLMs, outperforming the baseline method across several social groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are powerful tools that can help society, but they also have biases that make things unfair. Despite many attempts to fix these biases, they still exist and can be hard to spot. The good news is that some people have had success using multiple LLMs together to make them more fair. This paper introduces a new way of doing this that works better than before. |
