Investigating Expert-in-the-Loop LLM Discourse Patterns for Ancient Intertextual Analysis
This offers a scalable expert-in-the-loop approach for intertextual analysis in biblical studies, though it appears incremental in applying existing LLMs to this domain.
This study investigated using large language models (LLMs) to identify intertextual relationships in biblical Greek texts, finding they can detect direct quotations, allusions, and echoes but struggle with long passages and false dependencies.
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.