文本数据偏差检测与缓解——一个可扩展的流程及其实验评估
Textual Data Bias Detection and Mitigation -- An Extensible Pipeline with Experimental Evaluation
AI总结：
本文提出了一种可扩展的数据偏见检测与缓解流程，通过四个组件减少文本数据中的表示偏见和刻板印象，并通过实验评估展示其对模型偏见减少的影响。
摘要：
用于训练大型语言模型（LLMs）的文本数据表现出多方面的偏见表现，包括有害语言和偏斜的人口分布。例如，欧洲人工智能法案要求识别和缓解数据中对受保护群体的偏见，最终目标是防止不公平的模型输出。然而，缺乏实际指导和操作化方法。我们提出了一种综合的数据偏见检测和缓解流程，包含四个组件，针对两种数据偏见类型，即表示偏见和（显性）刻板印象，以配置敏感属性进行处理。首先，我们利用基于质量标准生成的LLM词表来检测相关群体标签。其次，使用人口表示分数量化表示偏见。第三，利用社会语言学信息指导的过滤检测和缓解刻板印象。最后，通过语法和上下文感知的反事实数据增强补偿表示偏见。我们使用性别、宗教和年龄的例子进行双重评估。首先，通过人工验证和基线比较评估每个组件在数据去偏见中的有效性。研究发现，我们成功地减少了文本数据集中的表示偏见和（显性）刻板印象。其次，通过多个模型（0.6B-8B参数）的偏见基准测试评估数据去偏见对模型偏见减少的影响。该评估揭示了当前评估方法学中的关键差距，并突显了针对显现模型偏见进行数据操纵的必要性。
英文摘要：
Textual data used to train large language models (LLMs) exhibits multifaceted bias manifestations encompassing harmful language and skewed demographic distributions. Regulations such as the European AI Act require identifying and mitigating biases against protected groups in data, with the ultimate goal of preventing unfair model outputs. However, practical guidance and operationalization are lacking. We propose a comprehensive data bias detection and mitigation pipeline comprising four components that address two data bias types, namely representation bias and (explicit) stereotypes for a configurable sensitive attribute. First, we leverage LLM-generated word lists created based on quality criteria to detect relevant group labels. Second, representation bias is quantified using the Demographic Representation Score. Third, we detect and mitigate stereotypes using sociolinguistically informed filtering. Finally, we compensate representation bias through Grammar- and Context-Aware Counterfactual Data Augmentation. We conduct a two-fold evaluation using the examples of gender, religion and age. First, the effectiveness of each individual component on data debiasing is evaluated through human validation and baseline comparison. The findings demonstrate that we successfully reduce representation bias and (explicit) stereotypes in a text dataset. Second, the effect of data debiasing on model bias reduction is evaluated by bias benchmarking of several models (0.6B-8B parameters), fine-tuned on the debiased text dataset. This evaluation reveals that LLMs fine-tuned on debiased data do not consistently show improved performance on bias benchmarks, exposing critical gaps in current evaluation methodologies and highlighting the need for targeted data manipulation to address manifested model bias.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
