LLMs are very helpful for transcribing handwritten historical documents, but sometimes those documents contain language/ideas that a perfectly aligned LLM will refuse to output. Sometimes as a hard refusal, sometimes (even worse) by subtly cleaning up the language.

In my experience the latest batch of models are a lot better at transcribing the text verbatim without moralizing about it (i.e. at "understanding" that they're fulfilling a neutral role as a transcriber), but it was a really big issue in the GPT-3/4 era.

I have a project where I'm using LLMs to parse data from PDFs with a very complicated tabular layout. I've been using the latest Gemini models (flash and pro) for their strong visual reasoning, and they've generally been doing a really good job at it.

My prompt states that their job is to extract the text exactly as it appears in the PDF. One data point to be extracted is the race of each person listed. In one case, someone's race was "Indian". Gemini decided to extract it as "Native American". So ridiculous.