Summary of Fairness in Large Language Models: a Taxonomic Survey, by Zhibo Chu et al.
Fairness in Large Language Models: A Taxonomic Survey
by Zhibo Chu, Zichong Wang, Wenbin Zhang
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 surveys the recent advances in large language models (LLMs) with a focus on fairness considerations. It begins by introducing LLMs and discussing the factors that contribute to bias in these models. The concept of fairness in LLMs is then categorized, summarizing metrics for evaluating bias and existing algorithms for promoting fairness. The paper also provides an overview of resources available for evaluating bias in LLMs, including toolkits and datasets. Finally, it discusses the existing research challenges and open questions in this area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how language models can be biased against certain groups. It starts by explaining what language models are and why they can be unfair. Then it talks about different ways to make sure these models are fair. The paper also mentions some tools and datasets that researchers can use to check for bias in language models. Overall, it’s an important look at how we can make sure AI is not hurting certain groups. |
