Ingesting PDFs and why Gemini 2.0 changes everything
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Re: Ingesting PDFs and why Gemini 2.0 changes everything
#290% accuracy +/- 10%? What could that be useful for, that’s awfully low.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#390% accuracy +/- 10%? What could that be useful for, that’s awfully low.
> accuracy is measured with the Needleman-Wunsch algorithm
> Crucially, we’ve seen very few instances where specific numerical values are actually misread. This suggests that most of Gemini’s “errors” are superficial formatting choices rather than substantive inaccuracies. We attach examples of these failure cases below [1].
> Beyond table parsing, Gemini consistently delivers near-perfect accuracy across all other facets of PDF-to-markdown conversion.
That seems fairly useful to me, no? Maybe not for mission critical applications, but for a lot of use cases, this seems to be good enough. I'm excited to try these prompts on my own later.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#490% accuracy +/- 10%? What could that be useful for, that’s awfully low.
This is "good enough" for Banks to use when doing due diligence. You'd be surprised how much noise is in the system with the current state of the art: algorithms/web scrapers and entire buildings of humans in places like India.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#590% accuracy +/- 10%? What could that be useful for, that’s awfully low.
Switching from manual data entry to approval
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#6Been toying with the flash model. Not the top model, but think it'll see plenty use due to the details. Wins on things other than top of benchmark logs
* Generous free tier
* Huge context window
* Lite version feels basically instant
However
* Lite model seems more prone to repeating itself / looping
* Very confusing naming e.g. {model}-latest worked for 1.5 but now its {model}-001? The lite has a date appended, the non-lite does not. Then there is exp and thinking exp...which has a date. wut?
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#7I wonder how this compares to open source models (which might be less accurate but even cheaper if self-hosted?), e.g. Llama 3.2. I'll see if I can run the benchmark.
Also regarding the failure case in the footnote, I think Gemini actually got that right (or at least outperformed Reducto) - the original document seems to have what I call a "3D" table where the third axis is rows within each cell, and having multiple headers is probably the best approximation in Markdown.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#890% accuracy +/- 10%? What could that be useful for, that’s awfully low.
This is "good enough" for Banks to use when doing due diligence. You'd be surprised how much noise is in the system with the current state of the art: algorithms/web scrapers and entire buildings of humans in places like India.
It's certainly pretty useful for discovery/information filtering purposes. I.e. searching for signal in the noise if you have a large dataset.
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#9Is there an AI platform where I can paste a snip of a graph and it will generate a n th order polynomial regression for me of the trace?
Re: Ingesting PDFs and why Gemini 2.0 changes everything
#10Been toying with the flash model. Not the top model, but think it'll see plenty use due to the details. Wins on things other than top of benchmark logs * Generous free tier * Huge context window * Lite version feels basically instant However * Lite model seems more prone to repeating itself / looping * Very confusing naming e.g. {model}-latest worked for 1.5 but now its {model}-001? The lite has a date appended, the…
> * Huge context window
But how well does it actually handle that context window? E.g. a lot of models support 200K context, but the LLM can only really work with ~80K or so of it before it starts to get confused.