InterIDEAS: Philosophical Intertextuality via LLMs

Yue Yang, Yinzhi Xu, Chenghao Huang, JohnMichael Jurgensen, Han Hu, Hao Wang


Abstract
The formation and circulation of ideas in philosophy have profound implications for understanding philosophical dynamism–enabling us to identify seminal texts, delineate intellectual traditions, and track changing conventions in the act of philosophizing. However, traditional analyses of these issues often depend on manual reading and subjective interpretation, constrained by human cognitive limits. We introduce InterIDEAS, a pioneering dataset designed to bridge philosophy, literary studies, and natural language processing (NLP). By merging theories of intertextuality from literary studies with bibliometric techniques and recent LLMs, InterIDEAS enables both quantitative and qualitative analysis of the intellectual, social, and historical relations embedded within authentic philosophical texts. This dataset not only assists the study of philosophy but also contributes to the development of language models by providing a training corpus that challenges and enhances their interpretative capacity.
Anthology ID:
2025.emnlp-main.1180
Volume:
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, Violet Peng
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
23162–23183
Language:
URL:
https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1180/
DOI:
Bibkey:
Cite (ACL):
Yue Yang, Yinzhi Xu, Chenghao Huang, JohnMichael Jurgensen, Han Hu, and Hao Wang. 2025. InterIDEAS: Philosophical Intertextuality via LLMs. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 23162–23183, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
InterIDEAS: Philosophical Intertextuality via LLMs (Yang et al., EMNLP 2025)
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https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1180.pdf
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