In our rapidly evolving digital sphere, the ability to discern media bias becomes crucial as it can shape public sentiment and influence pivotal decisions. The advent of large language models (LLMs), such as ChatGPT, noted for their broad utility in various natural language processing (NLP) tasks, invites exploration of their efficacy in media bias detection. Can ChatGPT detect media bias? This study seeks to answer this question by leveraging the Media Bias Identification Benchmark (MBIB) to assess ChatGPT's competency in distinguishing six categories of media bias, juxtaposed against fine-tuned models such as BART, ConvBERT, and GPT-2. The findings present a dichotomy: ChatGPT performs at par with fine-tuned models in detecting hate speech and text-level context bias, yet faces difficulties with subtler elements of other bias detections, namely, fake news, racial, gender, and cognitive biases.

翻译：在快速发展的数字时代，辨别媒体偏见的能力至关重要，因为它能塑造公众舆论并影响关键决策。以ChatGPT为代表的大型语言模型（LLMs）因其在多种自然语言处理（NLP）任务中的广泛应用而受到关注，这启发了对其在媒体偏见检测中有效性的探索。ChatGPT能否识别媒体偏见？本研究旨在借助媒体偏见识别基准（MBIB）评估ChatGPT在区分六类媒体偏见方面的能力，并将其与BART、ConvBERT和GPT-2等微调模型进行对比。研究结果呈现一种二分现象：ChatGPT在检测仇恨言论和文本层面语境偏见方面与微调模型表现相当，但在识别其他更微妙的偏见类型（即假新闻、种族、性别和认知偏见）时面临困难。