Large Language Models(LLMs) have revolutionized various applications in natural language processing (NLP) by providing unprecedented text generation, translation, and comprehension capabilities. However, their widespread deployment has brought to light significant concerns regarding biases embedded within these models. This paper presents a comprehensive survey of biases in LLMs, aiming to provide an extensive review of the types, sources, impacts, and mitigation strategies related to these biases. We systematically categorize biases into several dimensions. Our survey synthesizes current research findings and discusses the implications of biases in real-world applications. Additionally, we critically assess existing bias mitigation techniques and propose future research directions to enhance fairness and equity in LLMs. This survey serves as a foundational resource for researchers, practitioners, and policymakers concerned with addressing and understanding biases in LLMs.

本研究针对大型语言模型（LLMs）中的偏见问题进行全面调查，填补了当前研究中的空白。通过系统分类偏见的类型、来源和影响，并评估现有的缓解策略，本文提出了未来提升模型公平性和公正性的研究方向。研究结果为关注LLMs偏见的研究人员、从业者和政策制定者提供了重要资源。

大型语言模型中的偏见综合调查：现状与未来方向