用专家协作提升LLM识别古籍互文关系的能力
Investigating Expert-in-the-Loop LLM Discourse Patterns for Ancient Intertextual Analysis
- 让专家与大模型协同，分析圣经等古希腊文本的互文关系
- 模型能发现引述、典故和回响，但长段落和错误关联需人工校验
- 适合古典文献研究者，尤其关注文本间复杂关联的学者
本研究探讨大型语言模型（LLMs）在识别和分析圣经及通用希腊语文本中互文关系方面的潜力。通过评估模型在多种互文场景下的表现，研究发现这些模型能够检测出文本间的直接引用、典故和回响。模型生成新颖互文见解的能力表明其有潜力揭示新发现。然而，模型在处理长查询片段以及存在虚假互文依赖时表现不佳，强调了专家评审的重要性。文中提出的专家介入式方法为圣经文本内外复杂互文网络的研究提供了一种可扩展的路径。
原文摘要 · Abstract (English)
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.
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