Gemini developer experience was stupidly easy. Easy to add a file "part" to a prompt. Easy to focus on the main problem with weirdly high context window. Multi-modal so it handles a lot of issues for you (PDF image vs. PDF with data), etc. I can recommend it for the use case presented in this blog (ignoring the bounding boxes part)!

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?"

You could improve your accuracy further by adding some chain-of-thought to your prompt btw. e.g. Make each field in your json schema have a `reasoning` field beforehand so the model can CoT how it got to its answer. If you want to take it to the next level, `citations` in our experience also improves performance (and when combined with bounding boxes, is powerful for human-in-the-loop tooling).

Disclaimer: I started an LLM doc processing infra company (https://extend.app/)

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 "validate its performance, and deploy it with confidence into prod", but also sell that confidence with an SLA on top of it.

There's definitely space here to help the customer realize their extraction vision because it's still hard to scale this effectively on your own!

1 - https://scrapfly.io/extraction-api

OK, sure, we can parse a PDF reliably now, but now we need to act on that data. We need to store it, make sure it ends up with the right people who need to be notified that the data is available for their review. They then need to make decisions upon that data, possible requiring input from multiple stakeholders.

All that back and forth needs to be recorded and stored, along with the eventual decision and the all supporting documents and that whole bundle needs to be made available across multiple systems, which requires a bunch of ETLs and governance.

An LLM with a prompt doesn't replace all that.

I have worked in large systems, both in code and people, compilers, massive data processing systems, 10k business units.

I would say most acceleracionist/AI bulls/etc don't really understand the true essential complexity in software development. LLMs are being seen as a software development silver bullets, and we know what happens with silver bullets.

Is that what you mean and, if so, is there anything in particular you've seen that leads you to see these problems being solved well or on the 18 month timeline? That sounds interesting to look at to me and I'd love to know more.

You can't do point sampling to figure out where things are going. We have to look at the slope. People see a paper come out, look at the results and say, "this fails for x, y and z. doesn't work", that is now how scientific research works. This is why two minute papers has the tag line, "hold on to your papers ... two papers down the line ..."

Copy and paste the whole thread into a SOTA model and have meta me explain it.

They're doubting you because the non-digital portions of processes change at people/org speed.

Which is to say that changing a core business process is a year political consensus, rearchitecture, and change management effort, because you also have to coordinate all the cascading and interfacing changes.

You are thinking within the existing structures, those structures will evaporate. All along the software supply chain, processes will get upended, not just because of how technical assets will be created, but also how organizations themselves are structured and react and in turn how software is created and consumed.

This is as big as the invention of the corporation, the printing press and the industrial revolution.

I am not here to tutor people on this viewpoint or defend it, I offer it and everyone can do with it what they will.

B2B is different from B2C, so if one vendor has a handful of clients and they won't switch away, there's no obliterating happening.

What's opened up is even lower hanging fruit, on more trees. A SaaS company charging $3/month for the left-handed underwater basket weaver niche now becomes viable as a lifestyle business. The shovels in this could be supabase/similar, since clients can keep access to their data there even if they change frontends.

It's a bit late to start shifting now since it takes time. Ideally they should already have a product on the market.

But it is a good opportunity for a fast-moving OCR service to steal some customers from their competition. If I were working in this space, I'd be worried about that, and also about the possibility some of the LLM companies realize they could actually break into this market themselves right now, and secure some additional income.

EDIT:

I get the feeling that the main LLM suppliers are purposefully sticking to general-purpose APIs and refraining from competing with anyone on specific services, and that this goes beyond just staying focused. Some of potential applications, like OCR, could turn into money printers if they moved on them now, and they all could use some more cash to offset what they burn on compute. Is it because they're trying to avoid starting an "us vs. them" war until after they made everyone else dependent on them?

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.

Businesses that are just "today's LLM + our bespoke improvements" won't have legs.

- data is never shared between customers

- data never gets used for training

- we also configure data retention policies to auto-purge after a time period

As a mere occasional customer I've been scanning 4 to 5 pages of the same document layout every week in gemini for half a year, and every single week the results were slightly different.

To note the docs are bilingual so it could affect the results, but what stroke me is the lack of consistency, and even with the same model, running it two or three times in a row gives different results.

That's fine for my usage, but that sounds like a nightmare if everytime Google tweaks their model, companies have to reajust their whole process to deal with the discrepancies.

And sticking with the same model for multiple years also sound like a captive situation where you'd have to pay premium for Google to keep it available for your use.

Gemini 2.0 Flash seems better than 1.5 - https://deepmind.google/technologies/gemini/flash/

This is one of the reasons why open source offline models will always be part of the solution, if not the whole solution.

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.

Temperature changes the softmax equation[1], not weather or not you are sampling from the softmax result or choosing the highest probability. IBM's documentation corroborates this, saying you need to set do_sample to True in order for the temperature to have any effect, e.g., T changes how we sample, not if we sample [2].

A similar discussion on openai forum also claim that the RNG might be in a different state from run to run, although I am less sure about that [3]

[1] https://pelinbalci.com/2023/10/16/Temperature_parameter.html

[2] https://www.ibm.com/think/topics/llm-temperature#:~:text=The...

[3] https://community.openai.com/t/clarifications-on-setting-tem...

This approach should make the LLM deterministic regardless of the temperature chosen.

P.S. Choosing lower and lower temperatures will make the LLM more deterministic but it will never be totally deterministic because there will always be some probability in other tokens. Also it is not possible to use temperature as exactly 0 due to exp(1/T) blowup. Like I mentioned above, you could avoid fiddling with temperature and just decide to always choose token with highest probability for full determinism.

There are probably other more subtle things that might make the LLM non-deterministic from run to run though. It could be due to some non-deterministism in the GPU/CPU hardware. Floating point is very sensitive to ordering.

TL;DR for as much determinism as possible just choose token with highest probability (i.e. dont sample the distribution).

Is our tooling too bad for this?

Floating points are fundamentally too bad for this. We use them because they're fast, which usually more than compensates for inaccuracies FP math introduces.

(One, dealing with FP errors is mostly a fixed cost - there's a branch of CS/mathematics specializing in it, producing formally proven recipes for computing specific things in way that minimize or at least give specific bounds on errors. That's work that can be done once, and reused forever. Two, most programmers are oblivious to those issues anyway, and we've learned to live with the bugs :).)

When your parallel map-reduce is just doing matrix additions and multiplications, guaranteeing order of execution comes with serious overhead. For one, you need to have all partial results available together before reducing, so either the reduction step needs to have enough memory to store a copy of all the inputs, or it needs to block the units computing those inputs until all of them finish. Meanwhile, if you drop the order guarantee, then the reduction step just needs one fixed-size accumulator, and every parallel unit computing the inputs is free to go and do something else as soon as it's done.

So the price you pay for deterministic order is either a reduction of throughput or increase in on-chip memory, both of which end up translating to slower and more expensive hardware. The incentives strongly point towards not giving such guarantees if it can be avoided - keep in mind that GPUs have been designed for videogames (and graphics in general), and for this, floating point inaccuracies only matter when they become noticeable to the user.

Also if you're even considering fixed point math, you can use integer accumulators to add up your parallel chunks.

Model inference on GPU is mostly doing a lot of GPU equivalent of parallelized product on (X1, X2, X3, ... Xn), where each X is itself some matrix computed by a parallelized product of other matrices. Unless there's some explicit guarantee somewhere that the reduction step will pause until it gets all results so it can guarantee order, instead of reducing eagerly, each such step is a non-determinism transducer, turning undetermined execution order into floating point errors via commutation.

I'm not a GPU engineer so I don't know for sure, especially about the new cards designed for AI, but since reducing eagerly allows more memory-efficient design and improves throughput, and GPUs until recently were optimized for games (where FP accuracy doesn't matter that much), and I don't recall any vendor making determinism a marketing point recently, I don't believe GPUs suddenly started to guarantee determinism at expense of performance.

Why would the reduction step of a single neuron be split across multiple threads? That sounds slower and more complex than the naive method. And if you do decide to write code doing that, then just the code that reduces across multiple blocks needs to use integers, so pretty much no extra effort is needed.

Like, is there a nondeterministic-dot-product instruction baked into the GPU at a low level?

Not unless you control the underlying scheduler and force deterministic order; knowledge of all the code running isn't sufficient, as some factors affecting threading order are correlated with physical environment. For example, minute temperature gradient differences on the chip between two runs could affect how threads are allocated to CPU cores and order in which they finish.

> Why would the reduction step of a single neuron be split across multiple threads?

Doesn't have to, but can, depending on how many inputs it has. Being able to assume commutativity gives you a lot of flexibility in how you parallelize it, and allows you to minimize overhead (both in throughput and memory requirements).

> Like, is there a nondeterministic-dot-product instruction baked into the GPU at a low level?

No. There's just no dot-product instruction baked into GPU at low level that could handle vectors of arbitrary length. You need to write a loop, and that usually becomes some kind of parallel reduce.

I'm very confused by how you're interpreting the word "each" here.

> Being able to assume commutativity gives you a lot of flexibility in how you parallelize it, and allows you to minimize overhead (both in throughput and memory requirements).

Splitting up a single neuron seems like something that would only increase overhead. Can you please explain how you get "a lot" of flexibility?

> You need to write a loop, and that usually becomes some kind of parallel reduce.

Processing a layer is a loop within a loop.

The outer loop is across neurons and needs to be parallel.

The inner loop processes every weight for a single neuron and making it parallel sounds like extra effort just to increase instruction count and mess up memory locality and make your numbers less consistent.

Then, remember that GPUs are built around thousands of tiny parallel processors, each able to process a bunch (e.g. 16) parallel threads, but then the threads have to run in larger batches (SIMD-like), and there's a complex memory management architecture built-in, over which you only have so much control. Specific numbers of cores, threads, buffer sizes, as well as access patterns, differ between GPU models, and for optimal performance, you have to break down your computation to maximize utilization. Or rather, have the runtime do it for you.

This ain't an an FPGA, you don't get to organize hardware to match your network. If you have a 1000 neurons per hidden layer, then individual neurons likely won't fit on a single CUDA core, so you will have to split them down the middle, at least if you're using full-float math. Speaking of, the precision of the numbers you use is another parameter that adds to the complexity.

On the one hand, you have a bunch of mostly-linear matrix algebra, where you can tune precision. On the other hand, you have a GPU-model-specific number of parallel processors (~thousands), that can fit only so much memory, can run some specific number of SIMD-like threads in parallel, and most of those numbers are powers of two (or a multiple of), so you have also alignment to take into account, on top of memory access patterns.

By default, your network will in no way align to any of that.

It shouldn't be hard to see that assuming commutativity gives you (or rather the CUDA compiler) much more flexibility to parallelize your calculations by splitting it whichever way it likes to maximize utilization.

You can do very wide calculations on a single neuron if you want; throwing an entire SM (64 or 128 CUDA cores) at a single neuron is trivial to do in a deterministic way. And if you have a calculation so big you benefit from splitting it across SMs, doing a deterministic sum at the end will use an unmeasurably small fraction of your runtime.

And I'll remind you that I wasn't even talking about determinism across architectures, just within an architecture, so go ahead and optimize your memory layouts and block sizes to your exact card.

[1] https://arxiv.org/abs/2308.00951 pg. 4 [2] https://152334h.github.io/blog/non-determinism-in-gpt-4/

When you combine that fact with being part of a diverse batch of requests over an MoE model, outputs are non-deterministic.

One cannot possibly say that "Text extracted by a multimodal model cannot hallucinate"?

> accuracy was like 96% of that of the vendor and price was significantly cheaper.

I would like to know how this 96% was tested. If you use a human to do random sample based testing, well how do you adjust the random sample for variations in distribution of errors that vary like a small set of documents could have 90% of the errors and yet they are only 1% of the docs?

They are all probabilistic. You literally get back characters + confidence intervals. So when textract gives you back incorrect characters, is that a hallucination?

The hallucination in LLM extraction is much more subtle as it will rewrite full sentences sometimes. It is much harder to spot when reading the document and sounds very plausible.

We're currently working on a version where we send the document to two different LLMs, and use a 3rd if they don't match to increase confidence. That way you have the option of trading compute and cost for accuracy.

I’m interested to hear more about the validation process here. In my limited experience, I’ve sent the same “document” to multiple LLMs and gotten differing results. But sometimes the “right” answer was in the minority of responses. But over a large sample (same general intent of document, but very different possible formats of the information within), there was no definitive winner. We’re still working on this.

But on the flip side, layout is often times the biggest determinant of accuracy, and that's something LLMs do a way better job on. It doesn't matter if you have 100% accurate text from a table, but all that text is balled into one big paragraph.

Also the "pick the most plausible" approach is a blessing and a curse. A good example is the handwritten form here [1]. GPT 4o gets the all the email addresses correct because it can reasonably guess these people are all from the same company. Whereas AWS treats them all independently and returns three different emails.

[1] https://getomni.ai/ocr-demo

Traditional OCR is more likely to skip characters, or replace them with similar -looking ones, so you often get AL or A1 instead of AI for example. In other words, traditional spelling mistakes. LLMs can do anything from hallucinating new paragraphs to slightly changing the meaning of a sentence. The text is still grammatically correct, it makes sense in the context, except that it's not what the document actually said.

I once gave it a hand-written list of words and their definitions and asked it to turn that into flashcards (a json array with "word" and "definition"). Traditional OCR struggled with this text, the results were extremely low-quality, badly formatted but still somewhat understandable. The few LLMs I've tried either straight up refused to do it, or gave me the correct list of words, but entirely hallucinated the definitions.

Oh god, I wish speech to text engines would colour code the whole thing like a heat map to focus your attention to review where it may have over-enthusiastically guessed at what was said.

You no knot.

Speaking from experience, you need to double check "I" and "l" and "1" "0" and "O" all the time, accuracy seems to depend on the font and some other factors.

have a util script I use locally to copy some token values out of screenshots from a VMWare client (long story) and I have to manually adjust 9/times.

How relevant that is or isn't depends on the use case.

Also, the real solution to the problem should have been for the IRS to just pre-fill tax returns with all the accounting data that they obviously already have. But that would require the government to care.

So, maybe this century?

In this case, the reason for the misinformation is do to the lack of communication from the DOGE entity regarding their actions. Mr. Musk wrote via Tweet that he had "deleted" the digital services agency "18F" that develops the IRS Free File program and also deleted their X account.

https://apnews.com/article/irs-direct-file-musk-18f-6a4dc35a...

If indeed he did cut the agency, it remains to be see how long the application will be operational.

If Gemini can do semantic chunking at the same time as extraction, all for so cheap and with nearly perfect accuracy, and without brittle prompting incantation magic, this is huge.

The pre-step, chunking and semantic understanding is all that counts.

So I can ask Gemini to return chunks of variable size, where each chunk is a one complete idea or concept, without arbitrarily chopping a logical semantic segment into multiple chunks.

- BM25 to eliminate the 0 results in source data problem

- Longer term, a peek at Gwern's recent hierarchical embedding article. Got decent early returns even with fixed size chunks

For others interested in BM25 for the use case above, I found this thread informative.

https://news.ycombinator.com/item?id=41034297

We use it in combination with semantic but sometimes turn off the semantic part to see what happens and are surprised with the robustness of the results.

This would work less well for cross-language or less technical content, however. It's great for acronyms, company or industry specific terms, project names, people, technical phrases, and so on.

There should be laws that mandates that financial information be provided in a sensible format: even Office Open XML would be better than this insanity. Then we can redirect all this wasted effort into digging ditches and filling them back in again.

This is giving me hope that it's possible.

For this specific use case you can also try edgartools[1] which is a library that was relatively recently released that ingests SEC submissions and filings. They don't use OCR but (from what I can tell) directly parse the XBRL documents submitted by companies and stored in EDGAR, if they exist.

[1] https://github.com/dgunning/edgartools

"Correct errors in this OCR transcription".

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 generating not the output I wanted, at all.

If I am asking this black box to give me a JSON output containing keywords for a certain text, if it happens to be offensive, it refuses to do that.

How does one tackle with that?

The Gemini docs don't refect that change yet. https://discuss.ai.google.dev/t/safety-settings-2025-update-...

We have run into added content filters in Azure OpenAI on a different application, but we just put in a request to tune them down for us.

This sounds amazing & I'd love your input on our specific use case.

joelatoutboundin.com

Reaching reliability with LLM OCR might involve some combination of multiple LLMs (and keeping track of how they change), perhaps mixed with old-school algorithms, and random sample reviews by humans. They can tune this pipeline however they need at their leisure to eke out extra accuracy, and then put written guarantees on top, and still be cheaper for you long-term.

Marketing joke aside, maybe a hybrid approach could serve the vendor well. Best of both worlds if it reaps benefits or even have a look at hugging face for even more specialized aka better LLMs.

For most use cases in financial services, accurate data is very important.

I'm actually somewhat surprised Gemini didn't guess from context that LLC is much more likely?

I guess the OCR subsystem is intentionally conservative? (Though I'm sure you could do a second step on your end, take the output from the conservative OCR pass, and sent it through Gemini and ask it to flag potential OCR problems? I bet that would flag most of them with very few false positives and false negatives.)

https://static.foxnews.com/foxnews.com/content/uploads/2023/...

but were often written with typewriters long ago to get nice structured tabular output. Deals with text being split across lines and across pages just fine.

Literally whoever has the cheapest compute.

With the speed that AI models are improving these days, it seems like the 'moat' of a better model is only a few months before it is commoditized and goes to the cheapest provider.

I’ve been wanting to build a system that ingests pdf reports that reference other types of data like images, csv, etc. that can also be ingested to ultimately build an analytics database from the stack of unsorted data AB’s meta data but I have not found any time to do anything like that yet. What kind of tooling do you use to build your data pipelines?

A _killer_ awesome thing it does too is allow code specification in the config instead of through repeated attempts at prompts.

> Our prompt is currently very simple: "OCR this PDF into this format as specified by this json schema" and didn't require some fancy "prompt engineering" to contort out a result.

Also, OP mentioned fintech at the outset.

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 with a confidence score for free.

You then feed each box of text to a regular OCR model, also gives you a confidence score along with each prediction it makes.

You feed each image box into a multimodal model to describe what the image is about.

For tables, use a specialist model that does nothing but extract tables—models like GridFormer that aren't hyped to hell and back.

You then stitch everything together in an XML file because Markdown is for human consumption.

You now have everything extracted with flat XML markup for each category the object detection model knows about, along with multiple types of probability metadata for each bounding box, each letter, and each table cell.

You can now start feeding this data programmatically into an LLM to do _text_ processing, where you use the XML to control what parts of the document you send to the LLM.

You then get chunking with location data and confidence scores of every part of the document to put as meta data into the RAG store.

I've build a system that read 500k pages _per day_ using the above completely locally on a machine that cost $20k.

The longest part of the development timeline was establishing the accuracy rate and the ingestion pipeline, which itself is massively less complex than what your workflow sounds like: PDF -> Storage Bucket -> Gemini -> JSON response -> Database

Just to get sick with it we actually added some recusion to the Gemini step to have it rate how well it extracted, and if it was below a certain rating to rewrite its own instructions on how to extract the information and then feed it back into itself. We didn't see any improvement in accuracy, but it was still fun to do.

The table of costs in the blog post. At 500,000 pages per day the hardware fixed cost overcomes the software variable cost at day 240 and from then on you're paying an extra ~$100 per day to keep it running in the cloud. The machine also had to use extremely beefy GPUs to fit all the models it needed to. Compute utilization was between 5 to 10% which means that it's future proof for the next 5 years at the rate at which the data source was growing.

    | Model                       | Pages per Dollar |
    |-----------------------------+------------------|
    | Gemini 2.0 Flash            | ≈ 6,000          |
    | Gemini 2.0 Flash Lite       | ≈ 12,000*        |
    | Gemini 1.5 Flash            | ≈ 10,000         |
    | AWS Textract                | ≈ 1,000          |
    | Gemini 1.5 Pro              | ≈ 700            |
    | OpenAI 4o-mini              | ≈ 450            |
    | LlamaParse                  | ≈ 300            |
    | OpenAI 4o                   | ≈ 200            |
    | Anthropic claude-3-5-sonnet | ≈ 100            |
    | Reducto                     | ≈ 100            |
    | Chunkr                      | ≈ 100            |

>The longest part of the development timeline was establishing the accuracy rate and the ingestion pipeline, which itself is massively less complex than what your workflow sounds like: PDF -> Storage Bucket -> Gemini -> JSON response -> Database

Each company should build tools which match the skill level of their developers. If you're not comfortable training models locally with all that entails off the shelf solutions allow companies to punch way above their weight class in their industry.

However we do very much recommend storing the raw model responses for audit and then at least as vector embeddings to orient the expectation that the data will need to be utilized for vector search and RAG. Kind of like "while we're here why don't we do what you're going to want to do at some point, even if it's not your use-case now..."

The same reason I don't wait until it snows to buy snowboots. I know my environment, topography, scale, risk-profile, and costs, and can concieve of innumerable use-cases for when they will be necessary, even if it's only May, when snowboots happen to be on sale ;) What's a little closet space and the burden of locking my door when I leave the house in the interim?

Was this human-verified in some way? If not, how did you establish the facts-on-the-ground about accuracy?

In real world usage, many tables are badly misaligned. Headers are off. Lines are missing between rows. Some columns and rows are separated by colors. Cells are merged. Some are imported from Excel. There are dotted sub sections, tables inside cells etc. Claude (and now Gemini) can parse complex tables and convert that to meaningful data. Your solution will likely fail, because rules are fuzzy in the same way written language is fuzzy.

Recently someone posted this on HN, it's a good read: https://lukaspetersson.com/blog/2025/bitter-vertical/

> 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.

No, not like that, but often as nested Json or Xml. For financial documents, our accuracy was above 99%. There are many ways to do error checking to figure out which ones are likely to have errors.

> This is using exactly the wrong tools at every stage of the OCR pipeline, and the cost is astronomical as a result.

One should refrain making statements about cost without knowing how and where it'll be used. When processing millions of PDFs, it could be a problem. When processing 1000, one might prefer Gemini/other over spending engineering time. There are many apps where processing a single doc is say $10 in revenue. You don't care about OCR costs.

> I've build a system that read 500k pages _per day_ using the above completely locally on a machine that cost $20k.

The author presented techniques which worked for them. It may not work for you, because there's no one-size-fits-all for these kinds of problems.

AI founders will learn the bitter lesson

https://news.ycombinator.com/item?id=42672790 - 25 days ago, 263 comments

The HN discussion contains a lot of interesting ideas, thanks for the pointer!

1). I'm incompetent enough to ignore publicly available table benchmarks.

2). I'm incompetent enough to never look at poor quality data.

3). I'm incompetent enough to not create a validation dataset for all models that were available.

Needless to say you're wrong on all three.

My day rate is $400 + taxes per hour if you want to be run through each point and why VLMs like Gemini fail spectacularly and unpredictably when left to their own devices.

> My day rate is $400 + taxes per hour if you want to be run through each point

Great, thanks for sharing.

I integrated gemini recently to improve accuracy in certain blocks like tables. (get initial text, then pass to gemini to refine) Marker alone works about as well as gemini alone, but together they benchmark much better.

The rendering step for reports that humans got to see was a call to pandoc after the sxml was rendered to markdown - look ma we support powerpoint! - but it also allowed us to easily convert to whatever insane markup a given large (or small) language model worked best with on the fly.

[0] https://en.wikipedia.org/wiki/SXML

Marker output will be higher quality than docling output across most doc types, especially with the --use_llm flag. A few specific things we do differently:

  - We have hybrid mode with gemini that merges tables across pages, improves quality on forms, etc.
  - we run an ordering model, so ordering is better for docs where the PDF orde ris bad
  - OCR is a lot better, we train our own model, surya - https://github.com/VikParuchuri/surya
  - References and links
  - Better equation conversion (soon including inline)

As for markdown, great. Now how do you encode the meta data about the confidence of the model that the text says what it thinks it says? Becuase xml has this lovely thing called attributes that let's you keep a provenance record without a second database that's also readable by the llm.

If you already have a high optimized pipeline built yesterday, then sure keep using it.

But if you start dealing with PDF today, just use Gemini. Use the most human readable formats you can find because we know AI will be optimized on understanding that. Don't even think about "stitching XML files" blahblah.

That is impressive. However, if someone needs to read a couple of hundred pages per day, there's no point in setting all that up.

Also, you neglected to mention the cost of setting everything up. The machine cost $20k; but your time, and cost to train yolo8, probably cost more than that. If you want to compare costs (find a point where local implementation such as this is better ROI), you should compare fully loaded costs.

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.

If we had unlimited memory, compute and data we'd use a rank N tensor for an input of length N and call it a day.

Unfortunately N^N grows rather fast and we have to do all sorts of interesting engineering to make ML calculations complete before the heat death of the universe.

That is you'd need 5 exa yotta bytes to solve it.

Currently the whole world has around 200 zetabytes of storage.

I short for the next 120 years mnist will need mathematical tricks to be solved.

Its more about the information about the specific problem you are solving having less impact than techniques that target the compute. So in this case, breaking down how to parse a PDF in stages for your domain is involving specific expert knowledge of the domain, but training with attention is about efficient use of compute in general; with no domain expertise.

You are assuming you can match Gemini's performance, Google's engineering resources and costs being constant in to the future.

I'm not assuming. We already did, 18 months ago with better performance than the current generation of Gemini for our use case.

You're falling into the usual trap of thinking that because big tech spends big money it gets big results. It doesn't. To quote a friend who was a manager at google "If only I could get my team of 100 to be as productive as my first team of three.".

But there is no GitHub link or details on the implementation. Only model available seems to be one for removing weather effects from images: https://github.com/TaoWangzj/GridFormer

Could you care to expand on how you would use GridFormer for extracting tables from images? Seems like it's not as trivial as using something like Excalibur or Tabula, both which seem more battle-tested.

The number one take away we got was to use much larger images than anything that anyone else ever mentioned to get good results. A rule of thumb was that if you print the png of the image it should be easily readable from 2m away.

The actual model is proprietary and stuck in corporate land forever.

What you are describing is similar to how computer used to detect cats. You first extract edges, texture and gradient. Then use a sliding window and run a classifier. Then you use NMS to merge the bounding boxes.

You can lead a horse to water...

Digital fax services will generate pdf files, for example. They're just image data dumped into a pdf. Various scanners will also do so.

Something that was clearly a table now becomes a bunch of glphy's physically close to eachother vs a group of other glphys but when considered as a group is a box visually separated from another group of glphys but actually part of a table.