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Recent advances in NLP have led to the creation of powerful language modelsfor many languages including Ancient Greek and Latin. While prior work onClassical languages unanimously uses BERT, in this work we create four languagemodels for Ancient Greek that vary along two dimensions to study theirversatility for tasks of interest for Classical languages: we explore (i)encoder-only and encoder-decoder architectures using RoBERTa and T5 as strongmodel types, and create for each of them (ii) a monolingual Ancient Greek and amultilingual instance that includes Latin and English. We evaluate all modelson morphological and syntactic tasks, including lemmatization, whichdemonstrates the added value of T5's decoding abilities. We further define twoprobing tasks to investigate the knowledge acquired by models pre-trained onClassical texts. Our experiments provide the first benchmarking analysis ofexisting models of Ancient Greek. Results show that our models providesignificant improvements over the SoTA. The systematic analysis of model typescan inform future research in designing language models for Classicallanguages, including the development of novel generative tasks. We make all ourmodels available as community resources, along with a large curatedpre-training corpus for Ancient Greek, to support the creation of a larger,comparable model zoo for Classical Philology. Our models and resources areavailable at https://github.com/Heidelberg-NLP/ancient-language-models.

Exploring Large Language Models for Classical Philology