对比大模型与人类在历史解读中的文化偏见，发现模型共识更高但易因信息缺失出错。
Cultural Biases of Large Language Models and Humans in Historical Interpretation
- 比较人类与大模型对历史短文本的注释一致性
- 模型在历史事实解读上共识更强，但错误多源于信息遗漏或幻觉
- 适合研究历史认知偏差与数字人文教育的应用
本文比较了人类与大语言模型在历史注释上的表现。结果表明，两者均存在文化偏见，但大语言模型在短文本的历史事实解读上达成更高共识。人类分歧主要源于个人偏见，而模型分歧则多因信息缺失或产生幻觉。该研究对数字人文学具有重要意义，可支持大规模历史数据标注与量化分析，为跨语言模型的历史解读比较提供新路径，促进批判性思维训练。
原文摘要 · Abstract (English)
This paper compares historical annotations by humans and Large Language Models. The findings reveal that both exhibit some cultural bias, but Large Language Models achieve a higher consensus on the interpretation of historical facts from short texts. While humans tend to disagree on the basis of their personal biases, Large Models disagree when they skip information or produce hallucinations. These findings have significant implications for digital humanities, enabling large-scale annotation and quantitative analysis of historical data. This offers new educational and research opportunities to explore historical interpretations from different Language Models, fostering critical thinking about bias.
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