LangFair工具包助开发者评估大模型在具体场景中的偏见风险
LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use Cases
- 提供生成特定场景测试数据的自动化功能
- 支持根据使用场景计算偏差与公平性指标
- 内置决策框架帮助选择合适评估指标
大型语言模型在多个方面表现出偏见，可能对性别、种族、性取向或年龄等受保护属性群体造成不利影响。为填补这一空白，我们推出了LangFair——一个开源Python工具包，旨在为大模型实践者提供评估其特定应用场景中偏见与公平性风险的工具。该工具包可轻松生成由大模型对特定场景提示生成的响应数据集，并据此计算适用于该使用场景的各类指标。为辅助指标选择，LangFair还提供可操作的决策框架。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。