Comment by mattnewton
1 day ago
Nit, I’d say as someone who spend a fair amount of time doing it in the life insurance space, actually parsing arbitrary pdfs is very much not a solved problem without LLMs. Parsing a particular pdf is, at least until they change their table format or w/e.
I don’t think this idea is totally cursed, I think the implementation is. Instead of using it to shortcut filling in grades that the applicant could spot check, like a resume scraper, they are just taking the first pass from the LLM as gospel.
Right - the problem with PDF extraction is always the enormous variety of shapes that data might take in those PDFs.
If all the PDFs are the same format you can use plenty of existing techniques. If you have no control at all over that format you're in for a much harder time, and vLLMs look perilously close to being a great solution.
Just not the GPT-5 series! My experiments so far put Gemini 2.5 at the top of the pack, to the point where I'd almost trust it for some tasks - but definitely not for something as critical as extracting medical grades that influence people's ongoing careers!
-> put Gemini 2.5 at the top of the pack
I have come to the same conclusion having built a workflow that has seen 10 million+ non-standardized PDFs (freight bill of ladings) with running evaluations, as well as against the initial "ground-truth" dataset of 1,000 PDFs.
Humans: ~65% accurate
Gemini 1.5: ~72% accurate
Gemini 2.0: ~88% accurate
Gemini 2.5: ~92%* accurate
*Funny enough we were getting a consistent 2% improvement with 2.5 over 2.0 (90% versus 88%) until as a lark we decided to just copy the same prompt 10x. Squeezed 2% more out of that one :D
Gemini 3.0 is rumored to drop any day now, will be very interesting to see the score that gets for your benchmark here.
1 reply →
>Just not the GPT-5 series! My experiments so far put Gemini 2.5 at the top of the pack, to the point where I'd almost trust it for some tasks
Got it. The non-experts are holding it wrong!
The laymen are told "just use the app" or "just use the website". No need to worry about API keys or routers or wrapper scripts that way!
Sure.
Yet the laymen are expected to maintain a mental model of the failure modes and intended applications of Grok 4 vs Grok 4 Fast vs Gemini 2.5 Pro vs GPT-4.1 Mini vs GPT-5 vs Claude Sonnet 4.5...
It's a moving target. The laymen read the marketing puffery around each new model release and think the newest model is even more capable.
"This model sounds awesome. OpenAI does it again! Surely it can OCR my invoice PDFs this time!"
I mean, look at it:
And on and on it goes...
"The non-experts are holding it wrong!"
We aren't talking about non-experts here. Go read https://www.thalamusgme.com/blogs/methodology-for-creation-a...
They're clearly competent developers (despite mis-identifying GPT-5-mini as GPT-5o-mini) - but they also don't appear to have evaluated the alternative models, presumably because of this bit:
"This solution was selected given Thalamus utilizes Microsoft Azure for cloud hosting and has an enterprise agreement with them, as well as with OpenAI, which improves overall data and model security"
I agree with your general point though. I've been a pretty consistent voice in saying that this stuff is extremely difficult to use.
> The laymen
The solution architect, leads, product managers and engineers that were behind this feature are now laymen who shouldn't do their due diligence on a system to be used to do an extremely important task? They shouldn't test this system across a wide range of input pdfs for accuracy and accept nothing below 100%?
I've been doing PDF data extraction with LLMs at my day job, and my experience is to get them sufficiently reliable for a document of even moderate complexity (say, has tables and such, form fields, that kind of thing) you end up writing prompts so tightly-coupled to the format of the document that there's nothing but down-side versus doing the same thing with traditional computer vision systems. Like, it works (ask me again in a couple years as the underlying LLMs have been switched out, whether it's turned into wack-a-mole and long-missed data corruption issues... I'd bet it will) but using an LLM isn't gaining us anything at all.
Like, this company could have done the same projects we've been doing but probably gotten them done faster (and certainly with better performance and lower operational costs) any time in the last 15 years or so. We're doing them now because "we gotta do 'AI'!" so there's funding for it, but they could have just spent less money doing it with OpenCV or whatever years and years ago.
Eh, I guess we’re looked at different PDFs and models. Gemini 2.5 flash is very good, and Gemini 2.0 and Claude 3.7 were passable at parsing out complicated tables in image chunks, and we did have a fairly small prompt that worked >90% of cases. Where we had failures they were almost always in asking the model to do something infeasible (like parse a table where the header was on a previous, not provided page).
If you have a better way to parse PDFs using opencv or whatever, please provide this service and people will buy it for their RAG chat bots or to train vlms.
Would it be helpful if LLM creates bounding boxes for "traditional" OCR to work on? I.e. allowing extraction of information of arbitrary PDF as if it was a "particular pdf"