And then argues that the answer is that LLMs are not good enough yet. They can do 90% of the work, so there’s still a substantial amount of manual polish, and thus, increased costs.
there’s a lot of other things that i haven’t even touched on here, like the optimizations we compute ahead of time for specific patterns, how we detect match prefixes, the differences of UTF-16 and UTF-8 matching and unicode, what is the backwards compatibility story, how we can skip through text that others cannot. i haven’t even mentioned Hyperscan, which is a very interesting engine that is actually based on a completely different 1961 paper, and so on, there’s a lot to share so perhaps i will look into these topics in the future.
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Interested readers can follow this approach to train detectors for other domains—even general-purpose ones. Build an AIGC detector for academic papers, slap on a flashy frontend, and sell it as an “AI plagiarism checker” to clueless undergrads. If you make money, don’t forget to donate a little.
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