This paper investigates the efficacy of LatinBERT in the task of infilling ancient Latin inscriptions. We contrast the baseline LatinBERT model with a version fine-tuned specifically for this task. A comprehensive experimental design evaluates the influence of various lacunae features, such as their length and relative position within the text, on the infilling process. In contrast to the results presented in LatinBERT’s original publication, our findings indicate suboptimal performance. Interestingly, a parallel study of Greek inscriptions using models like PYTHIA and Ithaca demonstrated vastly superior performance in similar tasks. This disparity underscores the need for the development of more proficient models tailored for Latin inscriptions. Moreover, our study emphasizes the importance of robust and systematic evaluation methodologies to accurately assess model performance
