> Building your own evaluations makes sense if you're serving an LLM up to customers and want to know how it performs, but if you are the user... use it and see how it goes. It's all subjective anyway.
I'd really caution against this approach, mainly because humans suck at removing emotions and other "human" factors when judging how well something works, but also because comparing across models gets a lot easier when you can see 77/100 vs 91/100 as a percentage score, over your own tasks that you actually use the LLMs for. Just don't share this benchmark publicly once you're using it for measurements.
So what? I'm the one that's using it, I happen to be a human, my human factor is the only one that matters.
At this point anyone using these LLMs every day have seen those benchmark numbers go up without an appreciable improvement in the day to day experience.
> So what? I'm the one that's using it, I happen to be a human, my human factor is the only one that matters.
Yeah no you're right, if consistency isn't important to you as a human, then it doesn't matter. Personally, I don't trust my "humanness" and correctness is the most important thing for me when working with LLMs, so that's why my benchmarks focus on.
> At this point anyone using these LLMs every day have seen those benchmark numbers go up without an appreciable improvement in the day to day experience.
Yes, this is exactly my point. The benchmarks the makers of these LLMs seems to always provide a better and better score, yet the top scores in my own benchmarks have been more or less the same for the last 1.5 years, and I'm trying every LLM I can come across. These "the best LLM to date!" hardly ever actually is the "best available LLM", and while you could make that judgement by just playing around with LLMs, actually be able to point to specifically why that is, is something at least I find useful, YMMV.
I'd really caution against this approach, mainly because humans suck at removing emotions and other "human" factors when judging how well something works, but also because comparing across models gets a lot easier when you can see 77/100 vs 91/100 as a percentage score, over your own tasks that you actually use the LLMs for. Just don't share this benchmark publicly once you're using it for measurements.