大语言模型中的地缘政治偏见：当代语言模型眼中的“好”与“坏”国家

摘要

本研究通过分析大型语言模型（LLMs）对具有国家间对立视角（美国、英国、苏联和中国）的历史事件的解读，评估了其在各国立场上的地缘政治偏见。我们引入了一个包含中立事件描述及不同国家对比观点的新数据集。研究发现，模型存在显著的地缘政治偏见，倾向于特定国家的叙事。此外，简单的去偏见提示在减少这些偏见方面效果有限。通过操纵参与者标签的实验揭示了模型对归属的敏感性，有时会放大偏见或识别出不一致性，特别是在标签交换的情况下。这项工作凸显了LLMs中的国家叙事偏见，挑战了简单去偏见方法的有效性，并为未来的地缘政治偏见研究提供了框架和数据集。

English

This paper evaluates geopolitical biases in LLMs with respect to various countries though an analysis of their interpretation of historical events with conflicting national perspectives (USA, UK, USSR, and China). We introduce a novel dataset with neutral event descriptions and contrasting viewpoints from different countries. Our findings show significant geopolitical biases, with models favoring specific national narratives. Additionally, simple debiasing prompts had a limited effect in reducing these biases. Experiments with manipulated participant labels reveal models' sensitivity to attribution, sometimes amplifying biases or recognizing inconsistencies, especially with swapped labels. This work highlights national narrative biases in LLMs, challenges the effectiveness of simple debiasing methods, and offers a framework and dataset for future geopolitical bias research.

大语言模型中的地缘政治偏见：当代语言模型眼中的“好”与“坏”国家

Geopolitical biases in LLMs: what are the "good" and the "bad" countries according to contemporary language models

摘要

Support