LangFair: A Python Library for LLM Bias and Fairness Assessments
LangFair is a comprehensive Python library designed for conducting bias and fairness assessments of large language model (LLM) use cases. It addresses the limitations of static benchmark assessments by allowing users to tailor evaluations to specific use cases through a Bring Your Own Prompts (BYOP) approach. This ensures that the metrics computed reflect the true performance of LLMs in real-world scenarios.
Key Features:
- Use-Case Specific Evaluations: Customize bias and fairness assessments based on specific prompts relevant to your application.
- Output-Based Metrics: Focus on practical metrics for governance audits and real-world testing without needing access to internal model states.
- Comprehensive Metrics Suite: Includes toxicity metrics, stereotype metrics, counterfactual fairness metrics, and more.
- User-Friendly: Quickstart guides and example notebooks to help users get started easily.
Benefits:
- Enhanced Accuracy: Tailored assessments provide a more accurate representation of LLM performance.
- Flexibility: Users can adapt the library to various applications, including recommendation systems, classification, and text generation.
- Community Support: Open-source contributions and a dedicated development team enhance the library's capabilities.
Highlights:
- Supports a wide range of bias and fairness metrics.
- Offers semi-automated evaluation through the AutoEval class for streamlined assessments.
- Comprehensive documentation and example notebooks available for users.
