I've been using Llama models to identify cookie notices on websites, for the purpose of adding filter rules to block them in EasyList Cookie. Otherwise, this is normally done by, essentially, manual volunteer reporting.
Most cookie notices turn out to be pretty similar, HTML/CSS-wise, and then you can grab their `innerText` and filter out false positives with a small LLM. I've found the 3B models have decent performance on this task, given enough prompt engineering. They do fall apart slightly around edge cases like less common languages or combined cookie notice + age restriction banners. 7B has a negligible false-positive rate without much extra cost. Either way these things are really fast and it's amazing to see reports streaming in during a crawl with no human effort required.
Code is at https://github.com/brave/cookiemonster. You can see the prompt at https://github.com/brave/cookiemonster/blob/main/src/text-cl....
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