The pervasive spread of misinformation and disinformation in social media underscores the critical importance of detecting media bias. While robust Large Language Models (LLMs) have emerged as foundational tools for bias prediction, concerns about inherent biases within these models persist. In this work, we investigate the presence and nature of bias within LLMs and its consequential impact on media bias detection. Departing from conventional approaches that focus solely on bias detection in media content, we delve into biases within the LLM systems themselves. Through meticulous examination, we probe whether LLMs exhibit biases, particularly in political bias prediction and text continuation tasks. Additionally, we explore bias across diverse topics, aiming to uncover nuanced variations in bias expression within the LLM framework. Importantly, we propose debiasing strategies, including prompt engineering and model fine-tuning. Extensive analysis of bias tendencies across different LLMs sheds light on the broader landscape of bias propagation in language models. This study advances our understanding of LLM bias, offering critical insights into its implications for bias detection tasks and paving the way for more robust and equitable AI systems

翻译：社交媒体中错误信息和虚假信息的广泛传播凸显了检测媒体偏见的重要性。虽然稳健的大型语言模型已成为偏见预测的基础工具，但这些模型内部固有的偏见问题依然存在。本研究调查了LLM中偏见的存在与本质，及其对媒体偏见检测的影响。不同于仅关注媒体内容偏见检测的传统方法，我们深入探究LLM系统自身的偏见。通过细致检验，我们探讨LLM是否在政治偏见预测和文本续写任务中表现出偏见。此外，我们研究了跨不同主题的偏见，旨在揭示LLM框架内偏见表达的细微差异。重要的是，我们提出了包括提示工程和模型微调在内的去偏见策略。对不同LLM偏见倾向的广泛分析揭示了语言模型中偏见传播的宏观图景。本研究增进了我们对LLM偏见的理解，为偏见检测任务的影响提供了关键见解，并为构建更稳健、更公平的人工智能系统铺平了道路。