The open-source publishing of large language models (LLMs) has created many
possibilities for how anyone who understands language and has access to a
computer can interact with significant tools of artificial intelligence,
particularly in the context of learning and knowledge dissemination. However,
the utility of these models in specialized fields like Classics is still
largely unexplored. This project is an attempt to merge the knowledge of
Classics with the capabilities of artificial intelligence by finetuning an LLM
to cater to the specific needs of learners and professionals. The goal of this
project is to develop an LLM that not only reproduces contextual knowledge
accurately but also exhibits a consistent "personality" - and, indeed, has
consistent propriety - to appeal to a diverse audience who possess differing
levels of knowledge. A significant portion of this project was dedicated to
refining the dataset, following the principle of "garbage in, garbage out," to
ensure the model generates relevant, useful, and creative responses when given
a prompt (a statement, question, or single word). After training and
evaluation, my model's ability to handle a vast array of different types of
inputs and prompting exceeded expectations for a 355M parameter model, though
its occasional hallucinations (especially when set with a high temperature),
particularly in its assertions about historical events or its own identity,
make it seem somewhat capricious and more work in the form of continuous
finetuning will be undertaken.

该研究论文旨在通过优化一个大语言模型，结合古典学的知识和人工智能的能力，开发一个不仅能准确表达背景知识，而且具有一致性 “个性”、适合不同知识水平的广泛受众的大语言模型。研究着重于数据集的优化，训练和评估后，模型的处理能力超出了预期，但在某些情况下会出现幻觉，需要持续优化。