Recent improvements in large language models (LLMs) have significantly
enhanced natural language processing (NLP) applications. However, these models
can also inherit and perpetuate biases from their training data. Addressing
this issue is crucial, yet many existing datasets do not offer evaluation
across diverse NLP tasks. To tackle this, we introduce the Bias Evaluations
Across Domains (BEADs) dataset, designed to support a wide range of NLP tasks,
including text classification, bias entity recognition, bias quantification,
and benign language generation. BEADs uses AI-driven annotation combined with
experts' verification to provide reliable labels. This method overcomes the
limitations of existing datasets that typically depend on crowd-sourcing,
expert-only annotations with limited bias evaluations, or unverified AI
labeling. Our empirical analysis shows that BEADs is effective in detecting and
reducing biases across different language models, with smaller models
fine-tuned on BEADs often outperforming LLMs in bias classification tasks.
However, these models may still exhibit biases towards certain demographics.
Fine-tuning LLMs with our benign language data also reduces biases while
preserving the models' knowledge. Our findings highlight the importance of
comprehensive bias evaluation and the potential of targeted fine-tuning for
reducing the bias of LLMs. We are making BEADs publicly available at
this https URL
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近期大型语言模型取得重大突破，显著增强了自然语言处理应用，但这些模型也可能继承和持续传播来自训练数据的偏见。为了解决这个问题，我们介绍了 Bias Evaluations Across Domains (BEADs) 数据集，旨在支持各种自然语言处理任务，包括文本分类、偏见实体识别、偏见量化和良性语言生成。BEADs 使用 AI 驱动的注释结合专家验证来提供可靠的标签，克服了现有数据集的局限性。实证分析表明，BEADs 能够有效检测和减少不同语言模型的偏见，经过 BEADs 微调的较小模型在偏见分类任务中通常优于大型语言模型。然而，这些模型可能仍对特定人群存在偏见。使用我们的良性语言数据对大型语言模型进行微调也能减少偏见并保留模型的知识。我们的发现突出了全面偏见评估的重要性以及针对大型语言模型减少偏见的有针对性微调的潜力。我们将 BEADs 公开提供。