关键词: 大语言模型, 偏见溯源, 偏见识别, 偏见评估, 偏见去除

Abstract: Given the safety and ethical concerns arising from bias in Large Language Model (LLM), this review provides a comprehensive analysis of current research, techniques, and limitations related to bias in LLMs. The analysis is structured around three key aspects: bias identification, evaluation, and mitigation of bias. Firstly, three key techniques of LLM were examined to uncover the fundamental causes of intrinsic bias. Secondly, the types of bias present in LLM were categorized into linguistic bias, demographic bias, and evaluation bias, with characteristics and underlying causes explored. Thirdly, a systematic review of existing bias evaluation benchmarks was provided, discussing the strengths and weaknesses of the general-purpose, language-specific, and task-specific benchmarks. Finally, current bias mitigation techniques were analyzed, focusing on both model debiasing and data debiasing approaches while highlighting directions for future refinement. The findings highlights the research directions for bias in LLMs: evaluating bias from a multicultural perspective, developing lightweight bias mitigation techniques, and enhancing the interpretability of bias.

Key words:  large language model, bias tracing, bias identification, bias evaluation, bias mitigation