Summary of Fairness in Large Language Models in Three Hours, by Thang Doan Viet et al.
Fairness in Large Language Models in Three Hours
by Thang Doan Viet, Zichong Wang, Minh Nhat Nguyen, Wenbin Zhang
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 In this tutorial, the authors provide a comprehensive overview of recent advances in fair Large Language Models (LLMs), discussing the challenges and potential biases inherent in these models. The paper begins by introducing real-world case studies to demonstrate how LLMs can perpetuate discriminatory outcomes against marginalized populations. The concept of fairness in LLMs is then explored, including strategies for evaluating bias and algorithms designed to promote fairness. Additionally, the authors compile resources for assessing bias in LLLMs, such as toolkits and datasets, and discuss current research challenges and open questions in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models have become incredibly popular, but they can also be unfair. This tutorial explains why and how we can make them more fair. It starts by showing examples of where these models go wrong. Then it talks about what fairness means in this context and gives strategies for making sure language models are fair. The authors also share resources that can help us assess bias in these models and discuss some of the challenges they still need to overcome. |
