What is it about?
This tutorial covers the concept of fairness in Large Language Models (LLMs) like GPT and BERT, which are widely used in natural language processing tasks. While these models perform well, they can unintentionally favor certain groups over others, resulting in biased or discriminatory outcomes. The tutorial begins with real-world examples that illustrate the sources of these biases and introduces methods to evaluate and mitigate them. It then summarizes the latest strategies, tools, and datasets available for researchers to identify and address biases in LLMs. The goal is to help researchers understand how biases emerge in LLMs, how to measure them, and how to implement fairness in model outputs. Finally, we introduce the resources, challenges, and future directions in the field of fair LLMs. This tutorial is grounded in our surveys and established benchmarks, all available as open-source resources: https://github.com/LavinWong/Fairness-in-Large-Language-Model.
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Why is it important?
As LLMs are increasingly applied in high-stakes settings such as hiring, legal decisions, and medical diagnoses, the fairness of these models is critical to ensure that they do not unintentionally harm underrepresented groups. This tutorial addresses the unique challenges of fairness in LLMs, distinct from traditional machine learning models. By providing tools, case studies, and a structured approach to mitigating biases, this tutorial aims to equip researchers and practitioners with the knowledge to develop fairer, more reliable AI systems.
Read the Original
This page is a summary of: Fairness in Large Language Models in Three Hours, October 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3627673.3679090.
You can read the full text:
Resources
Fairness in Large Language Models: Paper and Code Repository
This is our related works that include the proposed taxonomy and surveys dedicated to various aspectThis repository consolidates our research efforts on fairness in Large Language Models (LLMs). It includes our proposed taxonomy, surveys, benchmarks, and various resources dedicated to understanding and addressing fairness in LLMs. Open-source resources like datasets, figures, and definitions are also provided to support researchers and practitioners working in this field.s of fairness in LLMs.
Fairness in Large Language Models Tutorial
Our tutorial website provides a comprehensive overview of fairness in Large Language Models (LLMs), addressing their biases and ethical implications. LLMs like BERT, GPT-3, and LLaMA demonstrate strong capabilities but often inherit biases from their training data and processes, leading to potential unfairness across sensitive attributes (e.g., age, gender, race). This tutorial explores the causes of bias in LLMs, reviews recent advancements in fair LLM methodologies, and highlights unique challenges in achieving fairness within these models. It fills a critical gap in resources by focusing specifically on LLMs, offering researchers and practitioners an in-depth, systematic guide to understanding and implementing fair LLM strategies.
Contributors
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