Comment by ColinEberhardt
Comment by ColinEberhardt 10 hours ago
Likewise. I have a nasty feeling that most AI agent deployments happen with nothing more than some cursory manual testing. Going with the ‘vibes’ (to coin an over used term in the industry).
A lot of "generative" work is like this. While you can come up with benchmarks galore, at the end of the day how a model "feels" only seems to come out from actual usage. Just read /r/localllama for opinions on which models are "benchmaxed" as they put it. It seems to be common knowledge in the local LLM community that many models perform well on benchmarks but that doesn't always reflect how good they actually are.
In my case I was until recently working on TTS and this was a huge barrier for us. We used all the common signal quality and MOS-simulation models that judged so called "naturalness" and "expressiveness" etc. But we found that none of these really helped us much in deciding when one model was better than another, or when a model was "good enough" for release. Our internal evaluations correlated poorly with them, and we even disagreed quite a bit within the team on the quality of output. This made hyperparameter tuning as well as commercial planning extremely difficult and we suffered greatly for it. (Notice my use of past tense here..)
Having good metrics is just really key and I'm now at the point where I'd go as far as to say that if good metrics don't exist it's almost not even worth working on something. (Almost.)