The LLM Lobotomy?

This was the perfect opportunity to share the evidence. I think undisclosed quantization is definitely a thing. We need benchmarks to be periodically re-evaluated to ward against this.

Providers should keep timestamped models fixed, and assign modified versions a new timestamp, and price, if they want. The model with the "latest" tag could change over time, like a Docker image. Then we can make an informed decision over which version to use. Companies want to cost optimize their cake and eat it too.

edit: I have the same complaint about my Google Home devices. The models they use today are indisputably worse than the ones they used five whole years ago. And features have been removed without notice. Qualitatively, the devices are no longer what I bought.

What's the conversation that you're looking to have here? There are fairly widespread claims that GPT-5 is worse than 4, and that's what the help article you've linked to says. I'm not sure how this furthers dialog about or understanding of LLMs, though, it reads to _me_ like this question just reinforces a notion that lots of people already agree with.

What's your aim here, sgt3v? I'd love to positively contribute, but I don't see how this link gets us anywhere.

It’s a good thing the author provided no data or examples. Otherwise, there might be something to actually talk about.

Is setting temperature to 0 even a valid way to measure LLM performance over time, all else equal?

I have a theory: all these people reporting degrading model quality over time aren't actually seeing model quality deteriorate. What they are actually doing is discovering that these models aren't as powerful as they initially thought (ie. expanding their sample size for judging how good the model is). The probabilistic nature of LLM produces a lot of confused thinking about how good a model is, just because a model produces nine excellent responses doesn't mean the tenth response won't be garbage.