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> If you use an LLM for something that you can directly compare to a non-LLM system, such as speech recognition or intent parsing, it's clear that the LLM is mo
by r00fus 3y ago
> If you use an LLM for something that you can directly compare to a non-LLM system, such as speech recognition or intent parsing, it's clear that the LLM is more trustworthy
Except for the hallucination problem, sure. How do you ensure the answer you get from an LLM is not a hallucination? It sure doesn't.
- pixl97 3y agoHow do you ensure any bits of data? You going to fist fight your uncle at the next family reunion when he promises you pigs can actually fly? There are plenty of bits of data that can be hard references to datasets or actual physics to assign their probability value. And there are other things that can't.
- r00fus 3y agoCompletely inefficient to chain logic through LLMs if they aren't baseline reliable.
- ianbicking 3y ago"How do you ensure the answer you get from an LLM is not a hallucination?" Intent parsing and speech recognition both frequently return inaccurate responses. It is true that with an LLM it can return something that is sensible but invented, where other systems typically return less inventive wrong answers. With any of these systems you want to balance the probability of an incorrect interpretation against the impact of the action being taken, and get confirmation based on that. That's pretty normal engineering and UX.