LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases
This addresses bias issues in LLMs for practitioners, but it is incremental as it builds on existing fairness assessment methods.
The authors tackled the problem of bias in large language models by introducing LangFair, an open-source Python package that helps practitioners evaluate bias and fairness risks, resulting in a tool for generating datasets and calculating metrics tailored to specific use cases.
Large Language Models (LLMs) have been observed to exhibit bias in numerous ways, potentially creating or worsening outcomes for specific groups identified by protected attributes such as sex, race, sexual orientation, or age. To help address this gap, we introduce LangFair, an open-source Python package that aims to equip LLM practitioners with the tools to evaluate bias and fairness risks relevant to their specific use cases. The package offers functionality to easily generate evaluation datasets, comprised of LLM responses to use-case-specific prompts, and subsequently calculate applicable metrics for the practitioner's use case. To guide in metric selection, LangFair offers an actionable decision framework.