探究专家在环的LLM话语模式在古代互文性分析中的应用
Investigating Expert-in-the-Loop LLM Discourse Patterns for Ancient Intertextual Analysis
AI总结：
本研究评估了LLMs在圣经希腊语文本互文性分析中的能力，发现其能检测引文、典故和回声，但在长查询和虚假依赖上存在局限，并提出专家在环方法以支持可扩展的互文研究。
摘要：
本研究探讨了大型语言模型（LLMs）在识别和考察圣经通用希腊语文本中互文关系方面的潜力。通过评估LLMs在各种互文性场景中的表现，本研究表明这些模型能够检测文本之间的直接引文、典故和回声。LLM生成新颖互文观察和联系的能力凸显了其发掘新见解的潜力。然而，该模型在处理长查询段落和包含虚假互文依赖时也存在困难，这强调了专家评估的重要性。所提出的专家在环方法为研究圣经语料库内外复杂互文网络提供了一种可扩展的互文研究方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
