I found the aeticle in a post on the fediverse, and I can’t find it anymore.
The reaserchers asked a simple mathematical question to an LLM ( like 7+4) and then could see how internally it worked by finding similar paths, but nothing like performing mathematical reasoning, even if the final answer was correct.
Then they asked the LLM to explain how it found the result, what was it’s internal reasoning. The answer was detailed step by step mathematical logic, like a human explaining how to perform an addition.
This showed 2 things:
-
LLM don’t “know” how they work
-
the second answer was a rephrasing of original text used for training that explain how math works, so LLM just used that as an explanation
I think it was a very interesting an meaningful analysis
Can anyone help me find this?
EDIT: thanks to @theunknownmuncher @lemmy.world https://www.anthropic.com/research/tracing-thoughts-language-model its this one
EDIT2: I’m aware LLM dont “know” anything and don’t reason, and it’s exactly why I wanted to find the article. Some more details here: https://feddit.it/post/18191686/13815095
The environmental toll doesn’t have to be that bad. You can get decent results from single high-end gaming GPU.
You can, but the stuff that’s really useful (very competent code completion) needs gigantic context lengths that even rich peeps with $2k GPUs can’t do. And that’s ignoring the training power and hardware costs to get the models.
Techbros chasing VC funding are pushing LLMs to the physical limit of what humanity can provide power and hardware-wise. Way less hype and letting them come to market organically in 5/10 years would give the LLMs a lot more power efficiency at the current context and depth limits. But that ain’t this timeline, we just got VC money looking to buy nuclear plants and fascists trying to subdue the US for the techbro oligarchs womp womp