mlsanityData to 14 Oct 2026

Same question, different history: language, national identity, and credit in large language models

arXiv 2606.23164 ↗First posted 22 Jun 2026cs.CLcited by 1

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Abstract (arXiv)

Who invented the radio, Russia's Alexander Popov or Italy's Guglielmo Marconi? Was the telephone the achievement of Bell in the United States or Meucci in Italy? Does printing belong to China's Bi Sheng or Germany's Gutenberg? The answer depends not only on historical record but also on language and perspective. We analyse eleven widely used large language models across 21 disputed inventions and discoveries, evaluated in twelve languages and 75,896 responses. While models generally acknowledge that credit is contested, query language systematically affects which claimant is surfaced. Lower-status claimants are more likely to appear when questions are asked in their associated language, whereas dominant Anglophone figures remain stable across languages. These patterns persist after controlling for response length, model differences, historical prominence, and levels of national commemoration. Language thus acts as a switch that activates different national versions of the same history, producing systematically different national memories from the same question. We interpret this as evidence that large language models function as distributed systems of cultural memory, where language conditions which histories become visible, contributing to a computational form of banal nationalism.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 40 references that are in the map. First posted on arXiv 22 Jun 2026.

Builds on

18 of 40 references.

References · 18

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  2. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  3. 2023Large Language Models are not Fair Evaluatorscited by 1,085
  4. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 956
  5. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 798
  6. 2023Whose Opinions Do Language Models Reflect?cited by 773
  7. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 686
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  10. 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 437
  11. 2024Knowledge Conflicts for LLMs: A Surveycited by 283
  12. 2024BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languagescited by 173
6 more
  1. 2023Benchmarking Cognitive Biases in Large Language Models as Evaluatorscited by 150
  2. 2023Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Modelscited by 118
  3. 2021On Measures of Biases and Harms in NLPcited by 81
  4. 2024Large Language Models Reflect the Ideology of their Creatorscited by 57
  5. 2025Language Models' Factuality Depends on the Language of Inquirycited by 14
  6. 2025Taxonomizing Representational Harms using Speech Act Theorycited by 7

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