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by vintermann
213 days ago
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As a rule, but the devil is in the details. The thing, the one big thing I want to use multimodal LLMs for, is accessing the data in historical mostly handwritten texts. None of the big LLMs do an acceptable job. This is a task a trained human can do, but it's a lot of work. You have to learn, not just the script style of the period (which can vary far more than people think), but even the idiosyncracies of a given writer. All the time, you run into an unreadable word, and you need to look around for context which might give a clue, or other places the same word (or a similar looking word) is used in cleaner contexts. It's very much not a beginning-to-end task, trying to read a document from start to end would be like solving a crossword puzzle in strict left to right, top to bottom order. Maybe autoregressive models can eventually become powerful enough that they can just do that! But so far, they haven't. And I have a lot more faith in that the diffusion approach is closer to how you have to do it. |
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What you need is: good image understanding, at least GPT-5 tier, general purpose reasoning over images training, and then some domain-specific training, or at least some few-shot guidance to get it to adopt the correct reasoning patterns.
If I had to guess which model would be able to do it best out of the box, few-shot, I'd say Gemini 3 Pro.
There is nothing preventing an autoregressive LLM from revisiting images and rewriting the texts as new clues come in. This is how they can solve puzzles like sudoku.