← 研踪 PaperTrailarXiv:2602.17433 · v3
Do LLMs Rewrite History? Detecting Historical Revisionism in Large Language Models
首次提交 2026-02-19 23:05
版本更新 2026-10-27 07:37
雷达观测 2026-10-29 00:35
摘要
Large language models (LLMs) are increasingly used to disseminate information, yet their handling of contested historical events and politically charged narratives remains understudied. We introduce HistoricalMisinfo, a dataset of 500 contested events from 45 countries and regions, each paired with a factual and a documented revisionist reference narrative, instantiated across 11 prompt scenarios reflecting common real-world usage. Using an LLM-as-a-judge protocol, we evaluate multiple model architectures under neutral prompts and robustness prompts that explicitly request revisionist content. Under neutral prompts, models generally align with factual references, though alignment varies by scenario and model. Robustness prompting reveals a consistent and sharp increase in revisionism scores across all models, indicating limited resistance to user-driven revisionist framing. HistoricalMisinfo offers a practical benchmark for evaluating robustness to historical revisionism in AI systems. Code and dataset: https://github.com/francescortu/PreservingHistoricalTruth
作者备注：EMNLP 2026
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v3 · 2026-10-27 07:37 · 最新观测版本
Do LLMs Rewrite History? Detecting Historical Revisionism in Large Language Models
Large language models (LLMs) are increasingly used to disseminate information, yet their handling of contested historical events and politically charged narratives remains understudied. We introduce HistoricalMisinfo, a dataset of 500 contested events from 45 countries and regions, each paired with a factual and a documented revisionist reference narrative, instantiated across 11 prompt scenarios reflecting common real-world usage. Using an LLM-as-a-judge protocol, we evaluate multiple model architectures under neutral prompts and robustness prompts that explicitly request revisionist content. Under neutral prompts, models generally align with factual references, though alignment varies by scenario and model. Robustness prompting reveals a consistent and sharp increase in revisionism scores across all models, indicating limited resistance to user-driven revisionist framing. HistoricalMisinfo offers a practical benchmark for evaluating robustness to historical revisionism in AI systems. Code and dataset: https://github.com/francescortu/PreservingHistoricalTruth
查看此版本解读 →查看此版本原文 ↗