> Crucially, we’ve seen very few instances where specific numerical values are actually misread.
"Very few" is way too many. This means it cannot be trusted, especially when it comes to financial data.
Ingesting PDFs and why Gemini 2.0 changes everything
331–340 of 456 posts
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#332I work in fintech and we replaced an OCR vendor with Gemini at work for ingesting some PDFs. After trial and error with different models Gemini won because it was so darn easy to use and it worked with minimal effort. I think one shouldn't underestimate that multi-modal, large context window model in terms of ease-of-use. Ironically this vendor is the best known and most successful vendor for OCR'ing this specific ty…
Is privacy a concern?
Why would it be? Their only concern is IPO.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#333Isn't it amazing how one company invented a universally spread format that takes structured data from an editor (except images obviously) and converts it into a completely fucked-up unstructured form that then requires expensive voodoo magic to convert back into structured data.
In my experience AWS Textextract does a pretty good job without using LLMs.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#334Earlier quoted context omitted.
How does it behave if the body of text is offensive or what if it is talking about a recipe to purify UF-6 gas at home? Will it stop doing what it is doing and enter lecturing mode? I am asking not to be cynical but because of my limited experience with using LLMs for any task that may operate on offensive or unknown input seems to get triggered by all sorts of unpredictable moral judgements and dragged into generati…
There are many settings for changing the safety level in Gemini API calls: https://ai.google.dev/gemini-api/docs/safety-settings
This is for anyone coming across this link later. In their latest SDKs, if you want to completely switch off their safety settings, the flag to use is 'OFF' and not 'BLOCK_NONE' as mentioned in the docs in the link above.
The Gemini docs don't refect that change yet. https://discuss.ai.google.dev/t/safety-settings-2025-update-...
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#335This is using exactly the wrong tools at every stage of the OCR pipeline, and the cost is astronomical as a result. You don't use multimodal models to extract a wall of text from an image. They hallucinate constantly the second you get past perfect 100% high-fidelity images. You use an object detection model trained on documents to find the bounding boxes of each document section as _images_; each bounding box comes…
Or, depending on your use case, you do it in one step and ask an LLM to extract data from a PDF.
What you describe is obviously better and more robust by a lot, but the LLM only approach is not "wrong". It’s simple, fast, easy to setup and understand, and it works. With less accuracy but it does work. Depending on the constraints, development budget and load it’s a perfectly acceptable solution.
We did this to handle 2000 documents per month and are satisfied with the results. If we need to upgrade to something better in the future we will, but in the mean time, it’s done.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#336Hmm I have been doing a but if this manually lately for a personal project. I am working on some old books that are far past any copyright, but they are not available anywhere on the net. (Being in Norwegian m makes a book a lot more obscure) so I have been working on creating ebooks out of them. I have a scanner, and some OCR processes I run things through. I am close to 85% from my automatic process. The pain of go…
I experienced something similar. My use case is I need to summarize bank statements (sums, averages, etc.). Gemini wouldn't do it, it said too many pages. When I asked the max number of supported pages, it says max is 14 pages. Attempted on both 2.0 flash and 2.0 pro in VertexAI console.
Try with https://aistudio.google.com
Think the page limit is a vertex thing
The only limit in reality is the number of input tokens taken to parse the pdf.
If those tokens + tokens for the rest of your prompt are under the context window limit, you're good.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#337Earlier quoted context omitted.
>A smart vendor will shift into that space - they'll use that LLM themselves It's a bit late to start shifting now since it takes time. Ideally they should already have a product on the market.
There's still time. The situation in which you can effectively replace your OCR vendor with hitting LLM APIs via a half-assed Python script ChatGPT wrote for you, has existed for maybe few months. People are only beginning to realize LLMs got good enough that this is an option. An OCR vendor that starts working on the shift today, should easily be able to develop, tune, test and productize an LLM-based OCR pipeline w…
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Re: Ingesting PDFs and why Gemini 2.0 changes everything
#338Earlier quoted context omitted.
This isn't really true unfortunately -- mixture of experts routing seems to suffer from batch non-determinism. No one has stated publicly exactly why this is, but you can easily replicate the behavior yourself or find bug reports / discussion with a bit of searching. The outcome and observed behavior of the major closed-weight LLM APIs is that a temperature of zero no longer corresponds to deterministic greedy sampli…
If temperature is zero, and weights are weights, where is the non-deterministic behavior coming from?
I have seen numbers come differently in JAX just depending on the batch size, simply because the compiler optimizes to a different sequence of operations on the hardware.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#339Earlier quoted context omitted.
> The problem then shifts from "can we extract this data from the PDF" to "how do we teach an LLM to extract the data we need, validate its performance, and deploy it with confidence into prod?" A smart vendor will shift into that space - they'll use that LLM themselves, and figure out some combination of finetunes, multiple LLMs, classical methods and human verification of random samples, that lets them not only "va…
>A smart vendor will shift into that space - they'll use that LLM themselves It's a bit late to start shifting now since it takes time. Ideally they should already have a product on the market.
Never underestimate the power of the second mover. Since the development is happening in the open, someone can quickly cobble up the information and cut directly to the 90% of the work.
Then your secret sauce will be your fine tunes, etc.
Like it or not AI/LLM will be a commodity, and this bubble will burst. Moats are hard to build when you have at least one open source copy of what you just did.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#340Earlier quoted context omitted.
> and every single week the results were slightly different. This is one of the reasons why open source offline models will always be part of the solution, if not the whole solution.
Inconsistency comes from scaling - if you are optimizing your infra to be cos effective you will arrive at same tradeoffs. Not saying it's not nice to be able to make some of those decisions on your own - but if you're picking LLMs for simplicity - we are years away from running your own being in the same league for most people.
And if you are not you wont.
You can decide if you change your local setup or not. You cannot decide the same of a service.
There is nothing inevitable about inconsistency in a local setup.