How LLM models try to understand Voynichese

[quimqu]

Hi Jorge,

Thank you for your comment. Just to clarify — the goal of this work was never to focus on prediction accuracy itself. In fact, it was inspired by some of your earlier remarks on my posts, especially about the "black box" nature of GPT models.

What I’ve been trying to do is open up that box a little and see what’s going on inside. That’s why I’ve focused on saliency — to visualize how much weight the model gives to each of the previous words when predicting the next one. I'm not trying to optimize performance, but rather to observe whether the model has learned any meaningful structure, or is just regurgitating memorized sequences.

What’s interesting (and a bit puzzling, even if it is something that is also found in natural languages) is how the saliency varies according to the word position in the block_size. The same word has different influence in the prediction of the next word at different positions. For example here:



word choiin has a big saliency at position 4 for predicting chol, but then at position 3 (the next position) it has the less saliency to predict cphey and increases again for positions 2 and 1. I hope that visualizing the MS so, can give some new perspectives.

This experiment was also inspired by something René once said — that instead of trying to decipher the Voynich, we should aim to understand the mechanism used to generate the language. So in a way, this work is a blend of your idea about model transparency and René’s hypothesis about language generation (and trying to find its mechanism)

Thanks again!