Abstract
Large Language Model (LLM) based summarization and text generation are increasingly used for producing and rewriting text, raising concerns about political framing in journalism where subtle wording choices can shape interpretation. Across nine state-of-the-art LLMs, we study political framing by testing whether LLMs' classification-based bias signals align with framing behavior in their generated summaries. We first compare few-shot ideology predictions against LEFT/CENTER/RIGHT labels. We then generate "steered" summaries under FAITHFUL, CENTRIST, LEFT, and RIGHT prompts, and score all outputs using a single fixed ideology evaluator. We find pervasive ideological center-collapse in both article-level ratings and generated text, indicating a systematic tendency toward centrist framing. Among evaluated models, Grok 4 is by far the most ideologically expressive generator, while Claude Sonnet 4.5 and Llama 3.1 achieve the strongest bias-rating performance among commercial and open-weight models, respectively.
Abstract (translated)
基于大型语言模型（LLM）的摘要和文本生成在制作和重写文本中的使用日益增加，引发了对新闻报道中政治框架问题的关注。细微的语言选择可能会塑造解释方式。通过对九种最先进的LLM进行研究，我们探讨了这些模型的政治框架倾向，方法是测试它们基于分类的偏见信号是否与其生成总结时的行为相符合。首先，我们将少量示例意识形态预测与LEFT/CENTER/RIGHT标签进行比较。然后，在FAITHFUL（忠实）、CENTRIST（中间派）、LEFT（左翼）和RIGHT（右翼）提示下生成“导向”摘要，并使用单一固定意识形态评估器对所有输出评分。 我们发现，无论是文章级别的评级还是生成的文本中都普遍存在意识形态上的中心化倾向，这表明这些模型倾向于采取中间立场。在经过评估的模型中，Grok 4 在表达意识形态方面表现最为突出，而Claude Sonnet 4.5 和Llama 3.1 分别是商业和开源模型中在偏见评级性能上最强的。
URL
https://arxiv.org/abs/2601.05835
