In this article, we evaluate several large language models (LLMs) on a word-level translation alignment task between Ancient Greek and English. Comparing model performance to a human gold standard, we examine the performance of four different LLMs, two open-weight and two proprietary. We then take the best-performing model and generate examples of word-level alignments for further finetuning of the open-weight models. We observe significant improvement of open-weight models due to finetuning on synthetic data. These findings suggest that open-weight models, though not able to perform a certain task themselves, can be bolstered through finetuning to achieve impressive results. We believe that this work can help inform the development of more such tools in the digital classics and the computational humanities at large.
