What is the point of using LLMs for the scrapping itself instead of using them to generate the boring code for mimicking HTTP requests, css/xpath selectors, etc?
I get it may be interesting for small tasks combined with a browser extension but for real scrapping just seems to be overkill and expensive.
It is potentially expensive, but here's a different take.
Instead of writing a bunch of selectors that break often, imagine just being able to write a paragraph telling the LLM to fetch the top 10 headlines and their links on a news site. Or to fetch the images, titles, and prices off a store front?
At my job we are scraping using LLMs. For a 10M sector of the company. GPT4 turbo has never not once out of 1.5 million API requests hallucinated. We however use it to parse data and interpret it from webpages, this is something you wouldn't be able to do with a regular scraper. Not well atleast.
I guess the claim is based on statistical sampling at reasonably high level to be sure that if there were hallucinations you would catch them? Or is there something else you're doing?
Do you have any workflow tools etc. to find hallucinations, I've got a project in backlog to build that kind of thing and would be interested in how you sort through bad and good results.
in this case we had 1.5 millioon ground truths for our testing purposes. we now have run it over 10 million, but i didnt want to claim it had 0 hallucinations on those as technically we cant say for sure, but considering the hallucination rate was 0% for 1.5 million when compared to ground truths im fairly confident.
This is pretty much what we're building at Skyvern. The only problem is that inference cost is still a little bit too high for scraping, but we expect that to change in the next year
I get it may be interesting for small tasks combined with a browser extension but for real scrapping just seems to be overkill and expensive.