AI vs Ancient Greek: The Translation Showdown

In an intriguing face-off between new AI and the ancient texts of Greek physician Galen, we find both promise and pitfalls. The study explores how commercial large language models (LLMs) like ChatGPT, Claude, and Gemini tackle the complex prose of Ancient Greek. Despite the grand potential of these models, the dense intricacies of ancient texts, they show clear limitations.

High Stakes in Historical Texts

Translating the work of Galen, a revered figure from 129-216 CE, is no small feat. His writings, a combination of expository and pharmacological texts, provided a formidable challenge. For those unfamiliar, Galen's texts aren't merely historical artifacts but treasure troves of medical knowledge. The LLMs achieved impressive results on passages with existing English translations, scoring an average of 95.2 out of 100 in quality assessments by domain experts. But the stakes change when the text hasn't been previously translated.

In the untouched pharmacological sections, the models' performance took a hit, clocking in at 79.9. Yet, this isn't the full story. Two passages, dense with rare terminology, caused the models to stumble dramatically. Is this a sign of a fundamental flaw in LLMs when facing specialized lexicons?

The Terminology Trap

What do these results tell us about the capabilities of AI in understanding and translating ancient languages? The study revealed that terminology rarity, determined by corpus frequency, emerged as the leading indicator of translation hiccups, with a correlation coefficient of -0.97. In simpler terms, the rarer the terminology, the higher the chance of failure. This speaks volumes about the current limitations of AI in handling specialized vocabulary.

Color me skeptical, but the claim that automated metrics can adequately replace human judgment doesn't survive scrutiny. While these metrics showed some correlation with human assessments, they struggled to distinguish between high-quality translations. What they're not telling you: automated metrics lack the nuanced understanding required for such complex texts.

The Bigger Picture

Why should we care about AI translating Ancient Greek? Beyond the historical implications, this study highlights the broader challenges AI faces in specialized domains. It serves as a reminder that while LLMs are powerful, they aren't infallible, especially when diving into niche fields rich with rare terminology.

Let's apply some rigor here. As we continue to integrate AI into various sectors, understanding its limitations is key. This isn't just about translating ancient texts, it's about ensuring we don't overestimate AI's capabilities where precision and expertise are key. For those in the tech community, this research is a call to arms to refine models, ensuring they can handle the depth and complexity of specialized knowledge. After all, if AI can’t translate Galen's medical insights accurately, what are the implications for other specialized fields?