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：社交媒体上错误信息和虚假信息的广泛传播突显了检测媒体偏见的至关重要性。虽然强大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偏见的担忧依然存在。本文中，我们研究了大型语言模型中偏见的存在及其性质，以及它对媒体偏见检测的影响。与仅关注媒体内容中偏见检测的传统方法不同，我们深入探讨了大型语言模型系统本身的偏见。通过细致审视，我们探究大型语言模型是否表现出偏见，特别是在政治偏见预测和文本续写任务中。此外，我们跨不同话题探索偏见，旨在揭示大型语言模型框架内偏见表达中的细微变化。重要的是，我们提出了去偏见策略，包括提示工程和模型微调。对不同大型语言模型偏见倾向的广泛分析，揭示了语言模型中偏见传播的更广阔图景。本研究增进了我们对大型语言模型偏见的理解，为其在偏见检测任务中的影响提供了关键见解，并铺平了构建更健壮、更公平人工智能系统的道路。