alt Hacker NewsWe've found they're surprisingly good if everyone on the call is using a decent headset.
The problems start when using conference room audio or someone is on their laptop mic. If they miss a word they never do unintelligible, they just start playing madlibs based on the rest of the sentence.
We just went through a round of 100+ (non-sensitive) VoC interviews and they really cut down the workload of compiling all of the feedback. If the audio was a little shaky though, we pretty much had to throw away the transcripts and do them from scratch like we used to.
> If they miss a word they never do unintelligible, they just start playing madlibs based on the rest of the sentence.
Imo this is the single biggest flaw of LLMs. They're great at a lot of things, but knowing when they're wrong (or don't have enough information to actually work on) is a critical flaw.
IMO there's nothing structural about why they shouldn't be able to spot this and correct themselves - I suspect it's a training issue. But presumably bots that infer context/fill in the dots rank better on what people like... at the cost of accuracy.