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keedayesterday at 8:28 PM1 replyview on HN

Yes, but the edge cases are infinite and so heuristics don't scale well. As an example, at some point you would likely find yourself with "dueling" heuristics, forcing you to tune them, which is brittle, or find yet another heuristic as a tie-breaker, which ratchets up the complexity. (I just spent a lot of time on an adjacent but much simpler problem before finally giving up on churning heuristics!)

As an example, many times it is impossible to determine the order of some words from just position data without considering the meanings of those words. This is why LLMs / VLMs are so much better at this task, because they can look at the document holistically like we can.

Also, funny that you mention patents, something I've worked on in the past as well! If you're looking only at US Patents, the USPTO data resource is much, much better: https://data.uspto.gov/home -- they provide the text in XML format (https://www.uspto.gov/learning-and-resources/xml-resources) which is also pretty complex but wayyyy easier to parse than PDFs!


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rayineryesterday at 10:47 PM

> This is why LLMs / VLMs are so much better at this task, because they can look at the document holistically like we can.

Totally agreed. But in this use case, PDFs are the working format, not just an archival format. An offline batch process to ingest the PDFs isn’t feasible. Unless there are some super fast LLMs I’m not aware of that can handle tens of PDF pages per second. It seems like Grok and Claude don’t try to read the PDF directly, they use pdftotext or some Python wrapper over pdfium. But maybe I’m missing something!