Framing in media critically shapes public perception by selectively emphasizing some details while downplaying others. With the rise of large language models in automated news and content creation, there is growing concern that these systems may introduce or even amplify framing biases compared to human authors. In this paper, we explore how framing manifests in both out-of-the-box and fine-tuned LLM-generated news content. Our analysis reveals that, particularly in politically and socially sensitive contexts, LLMs tend to exhibit more pronounced framing than their human counterparts. In addition, we observe significant variation in framing tendencies across different model architectures, with some models displaying notably higher biases. These findings point to the need for effective post-training mitigation strategies and tighter evaluation frameworks to ensure that automated news content upholds the standards of balanced reporting.

本研究针对大型语言模型（LLMs）在自动化新闻创作中可能引入或加剧的框架偏见问题进行了探讨。研究发现，在政治和社会敏感的背景下，LLMs的框架表现往往比人类作者更明显，并且不同模型架构之间的框架倾向差异显著。这一发现强调了在LLMs发布后需要有效的减偏策略和更严格的评估框架，以确保自动化新闻内容维持均衡报道的标准。

框架内外：大型语言模型生成的新闻标题是否比人类更具偏见？