| ▲ | lopsotronic 2 hours ago | ||||||||||||||||||||||||||||
The difference in response time - especially versus a regex running locally - is really difficult to express to someone who hasn't made much use of LLM calls in their natural language projects. Someone said 10,000x slower, but that's off - in my experience - by about four orders of magnitude. And that's average, it gets much worse. Now personally I would have maybe made a call through a "traditional" ML widget (scikit, numpy, spaCy, fastText, sentence-transformer, etc) but - for me anyway - that whole entire stack is Python. Transpiling all that to TS might be a maintenance burden I don't particularly feel like taking on. And on client facing code I'm not really sure it's even possible. | |||||||||||||||||||||||||||||
| ▲ | noprof6691 19 minutes ago | parent | next [-] | ||||||||||||||||||||||||||||
They're sending it to an llm anyway tho? Not sure why they wouldn't just add a sentiment field to the requested response shape. | |||||||||||||||||||||||||||||
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| ▲ | cyanydeez 2 hours ago | parent | prev | next [-] | ||||||||||||||||||||||||||||
So, think of it as a business man: You don't really care if your customers swear or whatever, but you know that it'll generate bad headlines. So you gotta do something. Just like a door lock isn't designed for a master criminal, you don't need to design your filter for some master swearer; no, you design it good enough that it gives the impression that further tries are futile. So yeah, you do what's less intesive to the cpu, but also, you do what's enough to prevent the majority of the concerns where a screenshot or log ends up showing blatant "unmoral" behavior. | |||||||||||||||||||||||||||||
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| ▲ | mlmonkey 38 minutes ago | parent | prev [-] | ||||||||||||||||||||||||||||
> Someone said 10,000x slower, but that's off - in my experience - by about four orders of magnitude. You do know that 10,000x _is_ four orders of magnitude, right? :-D | |||||||||||||||||||||||||||||
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