标题： BiasGuard：一种用于大型语言模型的推理增强型偏见检测工具 显示英文标题

标题： BiasGuard: A Reasoning-enhanced Bias Detection Tool For Large Language Models

摘要： 识别大型语言模型（LLM）生成内容中的偏见是确保LLM公平性的关键前提。现有的方法，如公平性分类器和基于LLM的裁判，面临着理解潜在意图困难以及缺乏公平性判断标准的限制。本文介绍了一种名为BiasGuard的新颖偏见检测工具，该工具明确分析输入并通过公平性规范进行推理以提供准确的判断。BiasGuard通过两阶段方法实现：第一阶段初始化模型以基于公平性规范进行显式推理，而第二阶段利用强化学习增强其推理和判断能力。我们在五个数据集上进行的实验表明，BiasGuard优于现有工具，提高了准确性并减少了过度公平性误判。我们还强调了增强推理决策的重要性，并提供了关于我们两阶段优化管道有效性的证据。 显示英文摘要

摘要： Identifying bias in LLM-generated content is a crucial prerequisite for ensuring fairness in LLMs. Existing methods, such as fairness classifiers and LLM-based judges, face limitations related to difficulties in understanding underlying intentions and the lack of criteria for fairness judgment. In this paper, we introduce BiasGuard, a novel bias detection tool that explicitly analyzes inputs and reasons through fairness specifications to provide accurate judgments. BiasGuard is implemented through a two-stage approach: the first stage initializes the model to explicitly reason based on fairness specifications, while the second stage leverages reinforcement learning to enhance its reasoning and judgment capabilities. Our experiments, conducted across five datasets, demonstrate that BiasGuard outperforms existing tools, improving accuracy and reducing over-fairness misjudgments. We also highlight the importance of reasoning-enhanced decision-making and provide evidence for the effectiveness of our two-stage optimization pipeline.