FairLMs：一个用于语言模型公平性的即用型库
FairLMs: A Turnkey Library for Fairness in Language Models
- Indiana University（印第安纳大学）
- Carnegie Mellon University（卡内基梅隆大学）
- Florida International University（佛罗里达国际大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
FairLMs是一个Python库，通过声明模型能力与输入要求，整合偏见衡量、缓解与证据检查，提供33个指标、14个缓解组件及诊断工具，支持多种架构与API，便于方法比较与工作流扩展。
AI中文摘要：
语言模型的公平性研究涉及衡量偏见、应用缓解方法以及检查评估所依据的证据。现有工具通过不同的接口提供互补功能，因此将它们组合起来需要先协调模型接口、证据格式、访问限制和结果类型，然后才能检查适用性或比较方法。我们介绍了FairLMs，一个Python库，它通过明确声明模型能力和输入要求来连接这些活动。它提供了33个内在和外在指标、14个跨越四个干预类别的缓解组件、14个数据集和评分工具诊断、针对三种Transformer架构和受支持的托管补全API的适配器，以及基准加载器。声明在执行前被检查，结果携带其获取时的配置，以便兼容组件可以组合，方法在共同协议下比较，工作流可扩展到新模型和数据集。源代码可在以下网址获取：此https URL。
英文摘要：
Fairness research on language models involves measuring bias, applying mitigation methods, and examining the evidence on which an evaluation rests. Existing tools offer complementary functionality through different interfaces, so combining them requires reconciling model interfaces, evidence formats, access constraints, and result types before applicability can be checked or methods compared. We introduce \textbf{FairLMs}, a Python library that connects these activities through explicit declarations of model capabilities and input requirements. It provides 33 intrinsic and extrinsic metrics, 14 mitigation components spanning four intervention categories, 14 dataset and scoring-instrument diagnostics, adapters for the three Transformer architectures and supported hosted completion APIs, and benchmark loaders. Declarations are checked before execution and results carry the configuration under which they were obtained, so that compatible components can be combined, methods compared under a common protocol, and workflows extended to new models and datasets. The source code is available at: https://github.com/FairLMs/FairLMs.
