FairPy: A Toolkit for Evaluation of Prediction Biases and their Mitigation in Large Language Models

TL;DR

FairPy is an open-source toolkit designed to evaluate and mitigate prediction biases in large language models like BERT and GPT-2, integrating various mathematical frameworks for bias analysis and debiasing.

Contribution

It introduces a modular, extensible toolkit that simplifies the evaluation and mitigation of biases in large language models, complementing existing mathematical frameworks.

Findings

Provides plug-and-play interfaces for bias evaluation

Supports implementation of debiasing algorithms

Open-source and easily integrable with existing models

Abstract

Recent studies have demonstrated that large pretrained language models (LLMs) such as BERT and GPT-2 exhibit biases in token prediction, often inherited from the data distributions present in their training corpora. In response, a number of mathematical frameworks have been proposed to quantify, identify, and mitigate these the likelihood of biased token predictions. In this paper, we present a comprehensive survey of such techniques tailored towards widely used LLMs such as BERT, GPT-2, etc. We additionally introduce Fairpy, a modular and extensible toolkit that provides plug-and-play interfaces for integrating these mathematical tools, enabling users to evaluate both pretrained and custom language models. Fairpy supports the implementation of existing debiasing algorithms. The toolkit is open-source and publicly available at: \href{https://github.com/HrishikeshVish/Fairpy}{https://github.com/HrishikeshVish/Fairpy}