@inproceedings{creanga-etal-2026-rashomon,
    title = "The Rashomon {W}ikipedia: A Data-Perspectivist Analysis of Divergent Historical Narratives",
    author = "Creanga, Claudiu  and
      Dinu, Liviu P.  and
      Dinu, Anca",
    editor = "Dudy, Shiran  and
      Abercrombie, Gavin  and
      Basile, Valerio  and
      Leonardelli, Elisa  and
      Frenda, Simona",
    booktitle = "Proceedings of the the fifth edition of {NLP}erspectives",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.nlperspectives-1.2/",
    doi = "10.63317/3sgqnffrw3hj",
    pages = "11--20",
    abstract = "Wikipedia aims to provide a unified, neutral record of history, yet its independent language editions often function as distinct epistemic communities, creating divergent narratives around contested events. This paper investigates cross-lingual historiographical bias by analyzing Wikipedia articles across five languages (Romanian, Hungarian, Russian, Turkish, and English) focusing on three contentious events in Romanian history: the Battle of Posada (1330), the Soviet occupation of Bessarabia (1940), and the Night Attack at T{\^a}rgoviște (1462). Using human annotators and Large Language Models (LLMs) to classify citation stance and quantify narrative evolution from 2005 to 2024, we identify a phenomenon of ``citation isolation''. In the case of the Battle of Posada, only 2 out of 119 citations were shared between language editions, with the Romanian edition exhibiting a 91{\%} pro-national bias compared to the balanced Hungarian edition. Longitudinal analysis reveals that these narratives are volatile and responsive to contemporary geopolitics, evidenced by a significant shift in the Russian framing of Bessarabia in 2024. Finally, we propose a ``Peace-Maker'' pipeline to automate conflict reconciliation. We demonstrate that while standard prompting leads models to hallucinate consensus, ``adversarial'' prompting, which explicitly instructs the model to preserve and attribute disagreement, achieves near-perfect neutrality scores."
}