Geopolitical biases in LLMs: what are the "good" and the "bad" countries according to contemporary language models
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
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| _version_ | 1866916802040168448 |
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| author | Salnikov, Mikhail Korzh, Dmitrii Lazichny, Ivan Karimov, Elvir Iudin, Artyom Oseledets, Ivan Rogov, Oleg Y. Loukachevitch, Natalia Panchenko, Alexander Tutubalina, Elena |
| author_facet | Salnikov, Mikhail Korzh, Dmitrii Lazichny, Ivan Karimov, Elvir Iudin, Artyom Oseledets, Ivan Rogov, Oleg Y. Loukachevitch, Natalia Panchenko, Alexander Tutubalina, Elena |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06751 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Geopolitical biases in LLMs: what are the "good" and the "bad" countries according to contemporary language models Salnikov, Mikhail Korzh, Dmitrii Lazichny, Ivan Karimov, Elvir Iudin, Artyom Oseledets, Ivan Rogov, Oleg Y. Loukachevitch, Natalia Panchenko, Alexander Tutubalina, Elena Computation and Language 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. |
| title | Geopolitical biases in LLMs: what are the "good" and the "bad" countries according to contemporary language models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.06751 |