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arXiv Computation and Language By Hope McGovern, Anna Dolganov, Samuel Belkadi, Guillaume Kunsch, Dimitris Vlitas, David A. Smith

Apollo Restore: A Foundation LLM for Historical Greek Optimized for Fill-in-the-Middle Restoration of Ancient Greek Texts

Read the original on arXiv Computation and Language →

Apollo Restore is a 24‑billion‑parameter large language model fine‑tuned from Mistral Small to fill in gaps in fragmentary Ancient Greek texts using a fill‑in‑the‑middle objective. It achieves state‑of‑the‑art performance on short lacunae, placing the correct restoration among its top twenty candidates for 80.6% of documentary‑papyrus, 54.6% of literary‑papyrus, and 61.0% of stone‑inscription gaps, outperforming previous models by significant margins. In blind expert evaluations, 20 specialists preferred Apollo Restore over the strongest baseline and judged its performance at least as good as human restorations in 77% of cases, while also improving the published reading of the Vesuvius‑carbonised papyrus P.Herc. 1667.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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