Speakers
Description
Generative AI has proved itself on many tasks where we can easily evaluate its performance, from transcribing modern documents and speech to linguistic annotation to mathematical proofs. To make progress on deeper research tasks in the humanities, new benchmarks and new methods of model interpretability should be developed. We present our work on the Apollo project for developing large models for textual and historical scholarship. Compiling the largest open-access dataset for ancient Greek (with Latin in progress), we train decoder models to restore texts with long, variable, and unknown gaps and surpass the performance of existing approaches that require exact estimates of lacuna size. We evaluate models on both agreement with published restorations and scholarly feedback on unrestored texts. Our models for Greek establish the foundations for scalable archival transcription of Greek historical documents, a key research objective of the Apollo project. We describe our approach to transcription and to linking transcription and restoration to interpretable evidence. Going beyond prediction to interpretation is necessary to advance our goals in philology, the study of historical documents, and the development of more broadly applicable AI.
Zoom link for the keynote: https://oeaw-ac-at.zoom.us/j/66241437371?pwd=TCk0D1DcZoZ5DcTORa9lPsCcFe6jbi.1