用多语言翻译公平性评估大模型政治偏见，发现主流政党更易被准确翻译。
Assessing the Political Fairness of Multilingual LLMs: A Case Study based on a 21-way Multiparallel EuroParl Dataset
- 基于多语言翻译公平性原则评估模型政治偏见
- 主流左右翼政党演讲翻译质量显著高于边缘政党
- 适用于多语言AI伦理与政治中立性研究者
大型语言模型（LLMs）的政治偏见通常通过模拟其对英语调查的回答来评估。本文提出一种新方法，基于多语言翻译中的公平性原则，系统比较欧洲议会（EP）演讲的翻译质量。结果发现，来自左右两大主流政党的演讲翻译更准确，而边缘政党则存在系统性偏差。这一研究依托全新的21语种多平行欧共体议会（EuroParl）数据集，包含4000万词、2.49亿字符，覆盖三年时间、1000多位发言者、7个国家、12个欧盟党派、25个欧盟委员会及数百个成员国政党。该数据集为多语言模型政治偏见研究提供了坚实基础。
原文摘要 · Abstract (English)
The political biases of Large Language Models (LLMs) are usually assessed by simulating their answers to English surveys. In this work, we propose an alternative framing of political biases, relying on principles of fairness in multilingual translation. We systematically compare the translation quality of speeches in the European Parliament (EP), observing systematic differences with majority parties from left and right being better translated than outsider parties. This study is made possible by a new, 21-way multiparallel version of EuroParl, the parliamentary proceedings of the EP, which includes the political affiliations of each speaker. The dataset consists of 1.5M sentences for a total of 40M words and 249M characters. It covers three years, 1000+ speakers, 7 countries, 12 EU parties, 25 EU committees, and hundreds of national parties.
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