用大模型检测政府文件中的语言偏见，效果优于通用生成模型。
Detecting Linguistic Bias in Government Documents Using Large language Models
- 构建荷兰议会文件偏见标注数据集DGDB，用于训练和评估。
- 微调的BERT模型在偏见检测上显著优于生成式大模型。
- 为多语言政府文本公平性研究提供可复用的数据与方法。
本文针对政府文件中偏见检测这一重要但研究不足的问题，提出荷兰政府偏见检测数据集（DGDB），源自荷兰议会公开文件并由专家标注。通过在该数据集上微调多个基于BERT的模型，并与生成式语言模型对比性能，结果表明微调模型表现更优。研究还进行了全面的错误分析，包括对预测结果的解释。实验证明，经过领域适配的微调模型在偏见识别任务中显著优于通用生成模型，凸显了高质量标注数据在跨语言偏见检测中的关键作用。本工作为提升治理公平性提供了可扩展的技术路径。
原文摘要 · Abstract (English)
This paper addresses the critical need for detecting bias in government documents, an underexplored area with significant implications for governance. Existing methodologies often overlook the unique context and far-reaching impacts of governmental documents, potentially obscuring embedded biases that shape public policy and citizen-government interactions. To bridge this gap, we introduce the Dutch Government Data for Bias Detection (DGDB), a dataset sourced from the Dutch House of Representatives and annotated for bias by experts. We fine-tune several BERT-based models on this dataset and compare their performance with that of generative language models. Additionally, we conduct a comprehensive error analysis that includes explanations of the models' predictions. Our findings demonstrate that fine-tuned models achieve strong performance and significantly outperform generative language models, indicating the effectiveness of DGDB for bias detection. This work underscores the importance of labeled datasets for bias detection in various languages and contributes to more equitable governance practices.
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