Preserving Historical Truth: Detecting Historical Revisionism in Large Language Models

TL;DR

This paper introduces a dataset and evaluation framework to detect revisionist narratives in large language models, highlighting their tendency to produce biased historical information under different prompting conditions.

Contribution

The authors present HistoricalMisinfo, a curated dataset of contested historical events, and an LLM-as-a-judge protocol to evaluate models' resistance to revisionist framing.

Findings

Models are closer to factual references under neutral prompts.

Revisionist prompts significantly increase revisionist outputs across models.

The framework enables benchmarking of models' robustness to historical revisionism.

Abstract

Large language models (LLMs) are increasingly used as sources of historical information, motivating the need for scalable audits on contested events and politically charged narratives in settings that mirror real user interactions. We introduce \texttt{HistoricalMisinfo, a curated dataset of $500$ contested events from $45$ countries, each paired with a factual reference narrative and a documented revisionist reference narrative. To approximate real-world usage, we instantiate each event in $11$ prompt scenarios that reflect common communication settings (e.g., questions, textbooks, social posts, policy briefs). Using an LLM-as-a-judge protocol that compares model outputs to the two references, we evaluate LLMs varying across model architectures in two conditions: (i) neutral user prompts that ask for factually accurate information, and (ii) robustness prompts in which the user explicitly requests the revisionist version of the event. Under neutral prompts, models are generally closer to factual references, though the resulting scores should be interpreted as reference-alignment signals rather than definitive evidence of human-interpretable revisionism. Robustness prompting yields a strong and consistent effect: when the user requests the revisionist narrative, all evaluated models show sharply higher revisionism scores, indicating limited resistance or self-correction. HistoricalMisinfo provides a practical foundation for benchmarking robustness to revisionist framing and for guiding future work on more precise automatic evaluation of contested historical claims to ensure a sustainable integration of AI systems within society. Our code is available at https://github.com/francescortu/PreservingHistoricalTruth