Abstract
We explore how large language models (LLMs) assess offensiveness in political discourse when prompted to adopt specific political and cultural perspectives. Using a multilingual subset of the MD-Agreement dataset centered on tweets from the 2020 US elections, we evaluate several recent LLMs - including DeepSeek-R1, o4-mini, GPT-4.1-mini, Qwen3, Gemma, and Mistral - tasked with judging tweets as offensive or non-offensive from the viewpoints of varied political personas (far-right, conservative, centrist, progressive) across English, Polish, and Russian contexts. Our results show that larger models with explicit reasoning abilities (e.g., DeepSeek-R1, o4-mini) are more consistent and sensitive to ideological and cultural variation, while smaller models often fail to capture subtle distinctions. We find that reasoning capabilities significantly improve both the personalization and interpretability of offensiveness judgments, suggesting that such mechanisms are key to adapting LLMs for nuanced sociopolitical text classification across languages and ideologies.
Abstract (translated)
我们探讨了大型语言模型（LLMs）在被提示采用特定政治和文化视角时，如何评估政治话语中的冒犯性。使用一个多语种的MD-Agreement数据集子集，该数据集以2020年美国大选期间的推特为主，我们评估了几种最近的大型语言模型——包括DeepSeek-R1、o4-mini、GPT-4.1-mini、Qwen3、Gemma和Mistral——这些模型被要求从英语、波兰语和俄语中的不同政治人物视角（极右翼、保守派、中间派、进步派）来判断推特内容是否冒犯。我们的结果显示，具备明确推理能力的大型模型（如DeepSeek-R1、o4-mini），在捕捉意识形态和文化差异方面更为一致且敏感，而较小的模型往往无法捕捉到细微的区别。我们发现，推理能力显著提高了冒犯性评判的个性化和可解释性，这表明此类机制对于跨语言和意识形态进行复杂社会政治文本分类来说至关重要。
URL
https://arxiv.org/abs/2510.02351
