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by vertnerd 195 days ago
I've also been working on half a dozen crates of old family letters. ChatGPT does well with them and is especially good at summarizing the letters. Unfortunately, all the output still has to be verified because it hallucinates words and phrases and drops lines here and there. So at this point, I still transcribe them by hand, because the verification process is actually more tiresome than just typing them up in the first place. Maybe I should just have ChatGPT verify MY transcriptions instead.
1 comments

It helps when you can see the confidence of each token, which downloadable weights usually gives you. Then whenever you (your software) detects a low confidence token, run over that section multiple times to generate alternatives, and either go with the highest confidence one, or manually review the suggestions. Easier than having to manually transcribe those parts at least.
Is there any way to do this with the frontier LLM's?
Ask them to mark low confidence words.
Do they actually have access to that info "in-band"? I would guess not. OTOH it should be straightforward for the LLM program to report this -- someone else commented that you can do this when running your own LLM locally, but I guess commercial providers have incentives not to make this info available.
Naturally, their "confidence" is represented as activations in layers close to output, so they might be able to use it. Research ([0], [1], [2], [3]) shows that results of prompting LLMs to express their confidence correlate with their accuracy. The models tend to be overconfident, but in my anecdotal experience the latest models are passably good at judging their own confidence.

[0] https://ieeexplore.ieee.org/abstract/document/10832237

[1] https://arxiv.org/abs/2412.14737

[2] https://arxiv.org/abs/2509.25532

[3] https://arxiv.org/abs/2510.10913

interesting... I'll give that a shot
It used to be that the answer was logprobs, but it seems that is no longer available.