调查大型语言模型中的隐性偏见：一项涵盖50多个LLM的大规模研究
Investigating Implicit Bias in Large Language Models: A Large-Scale Study of Over 50 LLMs
AI总结：
本研究基于LLM IAT偏见和决策偏见框架，对50多个LLM进行大规模评估，发现模型增大或更新并不自动减少隐性偏见，甚至可能放大偏见，凸显了标准化偏见评估与缓解策略的必要性。
摘要：
大型语言模型（LLMs）正被广泛应用于各种任务中，包括对AI系统偏见问题高度关注的行业决策过程。近期研究表明，即使LLMs通过了显性偏见评估，它们仍可能带有隐性偏见。基于LLM隐性联想测试（IAT）偏见和LLM决策偏见框架，本研究强调，较新或较大的语言模型并不会自动表现出偏见减少；在某些情况下，它们的偏见得分甚至高于前代模型，例如Meta的Llama系列和OpenAI的GPT模型。这表明，在没有刻意采取偏见缓解策略的情况下增加模型复杂性，可能会无意中放大现有偏见。不同提供商内部及跨提供商的偏见得分差异凸显了标准化评估指标和基准用于偏见评估的必要性。缺乏一致性表明，偏见缓解尚未成为模型开发中普遍优先的目标，这可能导致不公平或歧视性的结果。通过拓宽隐性偏见的检测范围，本研究为理解先进模型中存在的偏见提供了更全面的视角，并强调了解决这些问题以确保开发公平和负责任AI系统的关键重要性。
英文摘要：
Large Language Models (LLMs) are being adopted across a wide range of tasks, including decision-making processes in industries where bias in AI systems is a significant concern. Recent research indicates that LLMs can harbor implicit biases even when they pass explicit bias evaluations. Building upon the frameworks of the LLM Implicit Association Test (IAT) Bias and LLM Decision Bias, this study highlights that newer or larger language models do not automatically exhibit reduced bias; in some cases, they displayed higher bias scores than their predecessors, such as in Meta's Llama series and OpenAI's GPT models. This suggests that increasing model complexity without deliberate bias mitigation strategies can unintentionally amplify existing biases. The variability in bias scores within and across providers underscores the need for standardized evaluation metrics and benchmarks for bias assessment. The lack of consistency indicates that bias mitigation is not yet a universally prioritized goal in model development, which can lead to unfair or discriminatory outcomes. By broadening the detection of implicit bias, this research provides a more comprehensive understanding of the biases present in advanced models and underscores the critical importance of addressing these issues to ensure the development of fair and responsible AI systems.
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。
