Preserving Historical Truth: Detecting Historical Revisionism in Large Language Models

Large language models (LLMs) are increasingly consulted for historical information by citizens, journalists, and institutions, raising concerns about their tendency to reproduce or amplify historical revisionism: the distortion, omission, or reframing of established facts. We introduce \texttt{HistoricalMisinfo}, a curated dataset of $500$ contested events from $45$ countries, each paired with factual and revisionist narratives. To approximate real-world dissemination, we design $11$ prompt scenarios per event, capturing diverse ways historical content is elicited and framed. Using this benchmark, we evaluate multiple medium-sized LLMs and find systematic vulnerabilities: the prevalence of revisionist outputs varies across models, countries, and prompt types. \texttt{HistoricalMisinfo} provides a practical foundation for auditing the reliability of generative systems and for developing safeguards against the spread of revisionist narratives.