cracks? it doesn’t even exist. we figured this out a long time ago.
They are large LANGUAGE models. It’s no surprise that they can’t solve those mathematical problems in the study. They are trained for text production. We already knew that they were no good in counting things.
So do I every time I ask it a slightly complicated programming question
I’d be happy to help! There are 3 "w"s in the string “Howard likes strawberries”.
So I keep seeing people reference this… And I found it curious of a concept that LLMs have problems with this. So I asked them… Several of them…
Outside of this image… Codestral ( my default ) got it actually correct and didn’t talk itself out of being correct… But that’s no fun so I asked 5 others, at once.
What’s sad is that Dolphin Mixtral is a 26.44GB model…
Gemma 2 is the 5.44GB variant
Gemma 2B is the 1.63GB variant
LLaVa Llama3 is the 5.55 GB variant
Mistral is the 4.11GB Variant
So I asked Codestral again because why not! And this time it talked itself out of being correct…
Edit: fixed newline formatting.
Here’s the cycle we’ve gone through multiple times and are currently in:
AI winter (low research funding) -> incremental scientific advancement -> breakthrough for new capabilities from multiple incremental advancements to the scientific models over time building on each other (expert systems, LLMs, neutral networks, etc) -> engineering creates new tech products/frameworks/services based on new science -> hype for new tech creates sales and economic activity, research funding, subsidies etc -> (for LLMs we’re here) people become familiar with new tech capabilities and limitations through use -> hype spending bubble bursts when overspend doesn’t keep up with infinite money line goes up or new research breakthroughs -> AI winter -> etc…
Someone needs to pull the plug on all of that stuff.
