5 ms·
Determining whether the latest off the shelf LLMs are good enough should be straight forward because of this: “Some participants have dedicated years of their
by prng2021 2y ago
Determining whether the latest off the shelf LLMs are good enough should be straight forward because of this:
“Some participants have dedicated years of their lives to the program—like Alex Smith, a retiree from Pennsylvania. Over nine years, he transcribed more than 100,000 documents”
Have different LLMs transcribe those same documents and compare to see if the human or machine is or accurate and by how much.
- sandworm101 2y agoThis is not an LLM problem. It was solved years ago via OCR. Worldwide, postal services long ago deployed OCR to read handwitten addresses. And there was an entire industry of OCR-based data entry services, much of it translating the chicken scratch of doctor's handwiting on medical forms, long before LLMs were a thing.
- WillAdams 2y agoYes, but there was usually a fall-back mechanism where an unrecognized address would be shown on a screen to an employee who would type it so that it could then be inkjetted with a barcode.
- lukeschlather 2y agoLLMs improve significantly on state of the art OCR. LLMs can do contextual analysis. If I were transcribing these by hand, I would probably feed them through OCR + an LLM, then ask an LLM to compare my transcription to its transcription and comment on any discrepancies. I wouldn't be surprised if I offered minimal improvement over just having the LLM do it though.
- sandworm101 2y agoWhy assume that OCR does not involve context? OCR systems regularly use context. It doesnt require an LLM for a machine reading medical forms to generate and use a list of the hundred most common drugs appearing in a paticular place on a specific form. And an OCR reading envelopes can be directed to prefer numbers or letters depending on what it expects. Even if LLMs can push a 99.9% accuracy to 99.99, at least an OCR-based system can be audited. Ask an OCR vendor why the machine confused "Vancouver WA" and "Vancouver CA" and one can get a solid answer based in repeated testing. Ask an LLM vendor why and, at best, you'll get a shrug and some line citing how much better they were in all the other situations.
- iterance 2y agoAre you guessing, or are there results somewhere that demonstrate how LLMs improve OCR in practical applications?
- deleted 2y ago[deleted]
- Modified3019 2y agoSomeone linked this above https://trustdecision.com/resources/blog/revolutionizing-ocr-with-large-language-models https://trustdecision.com/resources/blog/revolutionizing-ocr... > Our internal tests reveal a leap in accuracy from 98.97% to 99.56%, while customer test sets have shown an increase from 95.61% to 98.02%. In some cases where the document photos are unclear or poorly formatted, the accuracy could be improved by over 20% to 30%. While a small percentage increase, when applied to massive amounts of text it’s a big deal.
- imtringued 2y agoIt's not a small percentage. The moment you OCR a book, you'll end up with hundreds to thousands of errors.
- dambi0 2y agoFor the addresses it might be a bit easier because they are a lot more structured and in theory and the vocabulary is a lot more limited. I’m less sure about medical notes although I’d suspect that there are fairly common things they are likely to say. Looking at the (admittedly single) example from the National Archives seems a bit more open than perhaps the other two examples. It’s not impossible thst LLMs could help with this
- prng2021 2y agoIt was never “solved” unless you can point me to OCR software that is 100% accurate. You can take 5 seconds to google “ocr with llm” and find tons of articles explaining how LLMs can enhance OCR. Here’s an example: https://trustdecision.com/resources/blog/revolutionizing-ocr-with-large-language-models https://trustdecision.com/resources/blog/revolutionizing-ocr...
- sandworm101 2y agoBy that standard, no problem has ever been solved by anyone. I prefer to believe that a great many everyday tech issues were in fact tackled and solved in the past by people who had never even heard of LLMs. So too many things were done in finance long before blockchains solved everything for us.
- VWWHFSfQ 2y agoOCR is not perfect. And therefore it is not "solved".
- Dylan16807 2y agoThat definition, solved=perfect, is not what sandworm meant and it's an irrelevant definition to this conversation because it's an impossible standard. Insisting we switch to that definition is just being unproductive and unhelpful. And it's pure semantics because you know what they meant.
- philipwhiuk 2y agoNot really, because this entire post is about that last fraction of a %.
- Dylan16807 2y agoIt's not, because then they wouldn't want humans, because humans can't do 100% either.
- iandanforth 2y agoFun fact, convolutional neural networks developed by Yann LeCunn were instrumental in that roll out!
- pinoy420 2y agoAgree. Sounds like not wanting to let go of a legacy