Investigating Expert-in-the-Loop LLM Discourse Patterns for Ancient Intertextual Analysis

Ray Umphrey, Jesse Roberts, Lindsey Roberts


Abstract
This study explores the potential of large language models (LLMs) for identifying and examining intertextual relationships within biblical, koine Greek texts. By evaluating the performance of LLMs on various intertextuality scenarios the study demonstrates that these models can detect direct quotations, allusions, and echoes between texts. The LLM’s ability to generate novel intertextual observations and connections highlights its potential to uncover new insights. However, the model also struggles with long query passages and the inclusion of false intertextual dependences, emphasizing the importance of expert evaluation. The expert-in-the-loop methodology presented offers a scalable approach for intertextual research into the complex web of intertextuality within and beyond the biblical corpus.
Anthology ID:
2024.nlp4dh-1.4
Volume:
Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities
Month:
November
Year:
2024
Address:
Miami, USA
Editors:
Mika Hämäläinen, Emily Öhman, So Miyagawa, Khalid Alnajjar, Yuri Bizzoni
Venue:
NLP4DH
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
31–40
Language:
URL:
https://aclanthology.org/2024.nlp4dh-1.4
DOI:
Bibkey:
Cite (ACL):
Ray Umphrey, Jesse Roberts, and Lindsey Roberts. 2024. Investigating Expert-in-the-Loop LLM Discourse Patterns for Ancient Intertextual Analysis. In Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities, pages 31–40, Miami, USA. Association for Computational Linguistics.
Cite (Informal):
Investigating Expert-in-the-Loop LLM Discourse Patterns for Ancient Intertextual Analysis (Umphrey et al., NLP4DH 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.nlp4dh-1.4.pdf