Computational Detection of Intertextual Parallels in Biblical Hebrew: A Benchmark Study Using Transformer-Based Language Models

Abstract: Identifying parallel passages in biblical Hebrew is foundational in biblical scholarship for uncovering intertextual relationships. Traditional methods rely on manual comparison, which is labor-intensive and prone to human error. This study evaluates the potential of pre-trained transformer-based LLMs, including E5, AlephBERT, MPNet, and LaBSE, for detecting textual parallels in the Hebrew Bible. Focusing on known parallels between the books of Samuel/Kings and Chronicles, I assessed each model's capability to generate word embeddings that delineate parallel from non-parallel passages. Utilizing cosine similarity and Wasserstein Distance measures, I found that E5 and AlephBERT show significant promise, with E5 excelling in parallel detection and AlephBERT demonstrating stronger non-parallel differentiation. These findings indicate that pre-trained models can enhance the efficiency and accuracy of detecting intertextual parallels in ancient texts, suggesting broader applications for ancient language studies.