Geopolitical biases in LLMs: what are the "good" and the "bad" countries
  according to contemporary language models
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.
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Mikhail Salnikov
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2025-06-11T08:34:47.630Z
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Mikhail Salnikov
user
msalnikov
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Dmitrii Korzh
6848fecf42e4f9106973f317
Ivan Lazichny
admin_assigned
2025-06-11T09:26:44.876Z
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Ivan
user
IvanLazichny
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Elvir Karimov
6848fecf42e4f9106973f319
Artyom Iudin
6848fecf42e4f9106973f31a
Ivan Oseledets
claimed_verified
2025-06-12T12:42:46.020Z
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Ivan Oseledets
user
oseledets
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Oleg Y. Rogov
claimed_verified
2025-08-28T08:57:44.864Z
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Oleg Y. Rogov
user
qubitter
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Alexander Panchenko
admin_assigned
2025-06-11T09:27:06.218Z
605473729d7c1d4d81b7e52b
https://cdn-avatars.huggingface.co/v1/production/uploads/1662046050710-605473729d7c1d4d81b7e52b.jpeg
Alexander Panchenko
user
apanc
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Natalia Loukachevitch
6848fecf42e4f9106973f31e
Elena Tutubalina
admin_assigned
2025-06-11T09:26:55.840Z
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Elena Tutubalina
user
tlenusik
Geopolitical biases in LLMs: what are the "good" and the "bad" countries
  according to contemporary language models
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.
LLMs exhibit significant geopolitical biases in their interpretation of historical events, and simple debiasing methods have limited effectiveness; a novel dataset for further research is provided.
LLMs geopolitical biases historical events national narratives debiasing prompts