利用大语言模型（LLMs）构建历史文献真实性评估的结构化体系
Structuring Authenticity Assessments on Historical Documents using LLMs
AI总结：
针对在线目录中历史文献真实性讨论为自由文本、难以大规模研究的问题，本研究提出结合LLMs与语义网技术的流程，无需训练即可提取并结构化真实性评估数据，生成带学者观点的争议文献目录，支撑复杂查询与分析。
摘要：
鉴于伪造行为在整个历史中广泛存在，学者们一直且仍在持续开展历史文献的真实性评估工作。然而，在线目录仅提供这些文献的描述性元数据，将关于其真实性的讨论归为自由文本格式，导致难以大规模研究这些评估结论。本研究探索从自然语言文本中生成文献真实性评估相关结构化数据的方法。我们的流程利用大语言模型（LLMs）无需训练即可选择、提取并分类相关主题的主张，同时结合语义网技术对LLM的输出结果进行结构化处理与类型验证。最终输出为一份真实性曾引发争议的文献目录，以及学者们对其真实性的观点。该流程可作为整合至目录中的宝贵资源，为针对数世纪以来这些争议演变的更复杂查询与分析提供空间。
英文摘要：
Given the wide use of forgery throughout history, scholars have and are continuously engaged in assessing the authenticity of historical documents. However, online catalogues merely offer descriptive metadata for these documents, relegating discussions about their authenticity to free-text formats, making it difficult to study these assessments at scale. This study explores the generation of structured data about documents' authenticity assessment from natural language texts. Our pipeline exploits Large Language Models (LLMs) to select, extract and classify relevant claims about the topic without the need for training, and Semantic Web technologies to structure and type-validate the LLM's results. The final output is a catalogue of documents whose authenticity has been debated, along with scholars' opinions on their authenticity. This process can serve as a valuable resource for integration into catalogues, allowing room for more intricate queries and analyses on the evolution of these debates over centuries.
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。
