The Austrian Academy of Science, Mistral, and Sail Reply plan to launch Apollo, an Ancient Greek LLM trained on ~600M historical Greek words, available for free

AI can now reconstruct fragmented historical texts, significantly speeding up scholarly research in Classics.

The Austrian Academy of Science, in collaboration with AI lab Mistral and tech firm Sail Reply, is launching Apollo, a large language model specifically for Ancient Greek. Trained on approximately 600 million historical Greek words from various sources, Apollo is designed to assist academics by rapidly identifying relevant papyrus fragments and suggesting words to fill gaps in damaged texts. This tool aims to accelerate research by automating parts of the document reconstruction process, which previously required extensive specialized knowledge. Apollo can adapt to different dialects and writing styles, such as Homeric Greek or inscriptions. While not expected to revolutionize broad historical understanding, it has the potential to uncover new details about ancient life and validate scholarly theories. The developers emphasize that human oversight remains crucial, with Apollo providing options for scholars to choose from rather than making definitive interpretations.

Apollo's development signifies a growing trend of specialized AI models tailored for niche academic disciplines, moving beyond general-purpose AI. This suggests a future where AI becomes an indispensable tool for historical research, not just in Classics but potentially for other ancient languages and fields requiring the analysis of large, complex corpora. The focus on enabling scholars to 'accelerate things' highlights a key value proposition of AI in academia: freeing up human expertise for higher-level analysis rather than tedious data processing.

The potential application of Apollo's methodology to other ancient languages like Latin or Egyptian indicates a broader market for AI-driven historical reconstruction. While the immediate beneficiaries are academics, the technology could eventually democratize access to historical knowledge by making complex textual analysis more manageable. The cautionary note about maintaining human competence is critical, suggesting that the future of AI in research lies in augmentation, not replacement, of human scholars.