GPT Models Fail to Find Language Structure in the Voynich Manuscript

[quimqu]

For the results to be comparable, we need to establish how Voynichese words correspond to plaintext words (and if they are words and not syllables, character combinations, etc). I think it makes sense repeating the experiment using comparable training sizes and output sizes expressed in Shannon bits

I see your point, but I wonder if that distinction might be overemphasized. Whether the tokens represent words, syllables, or even phonemes, any genuine writing system that encodes natural language should still reflect the underlying linguistic structure — which GPT models are designed to pick up on, regardless of granularity.

For example, Chinese uses characters that are often closer to morphemes or syllables than words, and Latin is highly inflected with longer words — but both still have strong statistical patterns in their sequential structure. If Voynichese encoded a real language at any level — word, syllable, or otherwise — we’d expect some degree of learnable co-occurrence patterns.

So while it’s an open question what exactly Voynichese tokens represent, the GPT’s failure to learn even short sequences suggests something deeper than just a mismatch in segmentation level. It might be structural.