跨语言检测大模型偏见，支持高/低资源语言的系统性评估
Mind the Language Gap: Automated and Augmented Evaluation of Bias in LLMs for High- and Low-Resource Languages
- 用自动翻译和改写扩展偏见测试覆盖多语言场景
- 在6种语言中测试4个顶尖大模型，覆盖7类歧视敏感类别
- 特别关注低资源语言，提升评估公平性
大型语言模型虽具备强大的自然语言处理能力，但常继承训练数据中的社会偏见。为此，我们提出多语言增强偏见测试（MLA-BiTe）框架，通过自动化翻译与改写技术，实现跨语言偏见的系统性评估。本研究在六种语言（含两种低资源语言）中，对四个领先的大语言模型进行了测试，覆盖七类敏感歧视类别，验证了 MLA-BiTe 在多语言环境下的有效性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have exhibited impressive natural language processing capabilities but often perpetuate social biases inherent in their training data. To address this, we introduce MultiLingual Augmented Bias Testing (MLA-BiTe), a framework that improves prior bias evaluation methods by enabling systematic multilingual bias testing. MLA-BiTe leverages automated translation and paraphrasing techniques to support comprehensive assessments across diverse linguistic settings. In this study, we evaluate the effectiveness of MLA-BiTe by testing four state-of-the-art LLMs in six languages -- including two low-resource languages -- focusing on seven sensitive categories of discrimination.
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