Instruct-Tuning Pretrained Causal Language Models for Ancient Greek Papyrology and Epigraphy
<|reference_start|>Instruct-Tuning Pretrained Causal Language Models for Ancient Greek Papyrology and Epigraphy: This article presents an experiment in fine-tuning a pretrained causal language model (Meta's Llama 3.1 8B Instruct) for aiding in three fundamental tasks of philological research: chronological and geographic attribution as well as text restoration in ancient Greek inscriptions and documentary papyri. Using a prompt-based instruct approach, the fine-tuned models surpass the state of the art in key metrics. For inscriptions, the models achieve a lower average character error rate (CER) of 22.5% (vs. 26.3%), while closely matching top-1 accuracy (60.9% vs. 61.8%) and top-20 accuracy (77.5% vs. 78.3%) for sequences up to 10 characters. They also provide a practical advantage by ignoring spaces during reconstruction, aligning better with the scriptio continua typically used in ancient written artifacts. In geographic attribution, the model outperforms previous benchmarks with a top-1 accuracy of 75.0% (vs. 70.8%) and a top-3 accuracy of 83.7% (vs. 82.1%). For dating, it achieves an average deviation of 26.2 years (vs. 29.3) and a median deviation of 1 year (vs. 3) from the actual date range. The models also set new baselines for documentary papyri, with a CER of 16.3%, a top-1 accuracy of 71.3%, and top-20 of 85.0% in text reconstruction; a top-1 accuracy of 66.4% and top-3 of 79.9% in geographic attribution; and, in chronological attribution, a deviation of 21.7 years from the actual termini post/ante quem, with a median deviation of 0 years.<|reference_end|>
@article{cullhed2024instruct-tuning,
    title={Instruct-Tuning Pretrained Causal Language Models for Ancient Greek
  Papyrology and Epigraphy},
    author={Eric Cullhed},
    journal={arXiv preprint arXiv:2409.13870},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13870},
    primaryClass={cs.CL cs.AI cs.LG}
}