The criticisms I hear are almost always gotchas, and when confronted with the benchmarks they either don’t actually know how they are built or don’t want to contribute to them. They just want to complain or seem like a contrarian from what I can tell.
Are LLMs perfect? Absolutely not. Do we have metrics to tell us how good they are? Yes
I’ve found very few critics that actually understand ML on a deep level. For instance Gary Marcus didn’t know what a test train split was. Unfortunately, rage bait like this makes money
Wait, what kind of metric are you talking about? When I did my masters in 2023 SOTA models where trying to push the boundaries by minuscule amounts. And sometimes blatantly changing the way they measure "success" to beat the previous SOTA
We can use little tricks here and there to try to make them better, but fundamentally they're about as good as they're ever going to get. And none of their shortcomings are growing pains - they're fundamental to the way an LLM operates.
and in 2023 and 2024 and january 2025 and ...
all those "walls" collapsed like paper. they were phantoms; ppl literally thinking the gaps between releases were permanent flatlines.
money obviously isn't an issue here, VCs are pouring in billions upon billions. they're building whole new data centres and whole fucking power plants for these things; electricity and compute aren't limits. neither is data, since increasingly the models get better through self-play.
>fundamentally they're about as good as they're ever going to get
one trillion percent cope and denial
And yes, it often is small things that make models better. It always has been, bit by slow they get more powerful, this has been happening since the dawn of machine learning
They're also trained on random data scraped off the Internet which might include benchmarks, code that looks like them, and AI articles with things like chain of thought. There's been some effort to filter obvious benchmarks but is that enough? I cant know if the AI's are getting smarter on their own or more cheat sheets are in the training data.
Just brainstorming, one thing I came up with is training them on datasets from before the benchmarks or much AI-generated material existed. Keep testing algorithmic improvements on that in addition to models trained on up to date data. That might be a more accurate assessment.
A lot of the trusted benchmarks today are somewhat dynamic or have a hidden set.
"somewhat dynamic or have a hidden set"
Are there example inputs and outputs for the dynamic ones online? And are the hidden sets online? (I haven't looked at benchmark internals in a while.)