The recent advent of powerful Large-Language Models (LLM) provides a new
conversational form of inquiry into historical memory (or, training data, in
this case). We show that by augmenting such LLMs with vector embeddings from
highly specialized academic sources, a conversational methodology can be made
accessible to historians and other researchers in the Humanities. Concretely,
we evaluate and demonstrate how LLMs have the ability of assisting researchers
while they examine a customized corpora of different types of documents,
including, but not exclusive to: (1). primary sources, (2). secondary sources
written by experts, and (3). the combination of these two. Compared to
established search interfaces for digital catalogues, such as metadata and
full-text search, we evaluate the richer conversational style of LLMs on the
performance of two main types of tasks: (1). question-answering, and (2).
extraction and organization of data. We demonstrate that LLMs semantic
retrieval and reasoning abilities on problem-specific tasks can be applied to
large textual archives that have not been part of the its training data.
Therefore, LLMs can be augmented with sources relevant to specific research
projects, and can be queried privately by researchers.

通过给强大的大型语言模型（LLM）增加来自高度专业化学术源的向量嵌入，我们展示了一种基于对话的方法可以使历史学家和其他人文学科的研究人员能够使用 LLMs 来检查不同类型文档的自定义语料库，并评估了 LLMs 在问题回答和数据提取组织两大任务上的表现。