Pantheon 1.0, a manually verified dataset of globally famous biographies
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Abstract (arXiv)
We present the Pantheon 1.0 dataset: a manually verified dataset of individuals that have transcended linguistic, temporal, and geographic boundaries. The Pantheon 1.0 dataset includes the 11,341 biographies present in more than 25 languages in Wikipedia and is enriched with: (i) manually verified demographic information (place and date of birth, gender) (ii) a taxonomy of occupations classifying each biography at three levels of aggregation and (iii) two measures of global popularity including the number of languages in which a biography is present in Wikipedia (L), and the Historical Popularity Index (HPI) a metric that combines information on L, time since birth, and page-views (2008-2013). We compare the Pantheon 1.0 dataset to data from the 2003 book, Human Accomplishments, and also to external measures of accomplishment in individual games and sports: Tennis, Swimming, Car Racing, and Chess. In all of these cases we find that measures of popularity (L and HPI) correlate highly with individual accomplishment, suggesting that measures of global popularity proxy the historical impact of individuals.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2015.
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Citing papers per year: 2024: 2; 2025: 5; 2026: 1.
8 papers.
- 2024Large Language Models Reflect the Ideology of their Creatorscited by 56
- 2025What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practicescited by 11
- 2024Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Languagecited by 3
- 2026SALMUBench: A Benchmark for Sensitive Association-Level Multimodal Unlearningcited by 3
- 2025Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Boundscited by 2
- 2025ADAM: A Diverse Archive of Mankind for Evaluating and Enhancing LLMs in Biographical Reasoningcited by 1
- 2025Engagement Undermines Safety: How Stereotypes and Toxicity Shape Humor in Language Modelscited by 1
- 2025Differentiating Emigration from Return Migration of Scholars Using Name-Based Nationality Detection Modelscited by 0
- 2026SALMUBench: A Benchmark for Sensitive Association-Level Multimodal Unlearning27 Mar 2026
- 2025Engagement Undermines Safety: How Stereotypes and Toxicity Shape Humor in Language Models21 Oct 2025
- 2025ADAM: A Diverse Archive of Mankind for Evaluating and Enhancing LLMs in Biographical Reasoning26 Sep 2025
- 2025Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds27 Jun 2025
- 2025Differentiating Emigration from Return Migration of Scholars Using Name-Based Nationality Detection Models9 May 2025
- 2025What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices4 Apr 2025
- 2024Large Language Models Reflect the Ideology of their Creators24 Oct 2024
- 2024Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language25 Mar 2024