LLMs: Optimizers or Overrated? New Findings Shake Up AI Assumptions

JUST IN: The honeymoon phase with large language models (LLMs) might be over. These giant neural networks are often hailed as the next big thing, but recent experiments expose some chinks in their shiny armor.

Greedy Optimizers in Disguise

First off, let's talk about black-box optimization. When left to their own devices, LLMs operate as greedy optimizers. They're not the nuanced problem solvers we hoped for. Instead, they chase immediate gains without considering the broader picture. It's like watching a kid grab all the candy without a thought for tomorrow's sugar crash.

Zero-Shot Kernel Generation: Size Doesn't Matter

Sources confirm: In zero-shot kernel generation, feeding in explicit input-size data doesn't move the needle. Models hit the same parameters regardless of input size or temperature settings. It seems the size instructions are invisible to these hefty algorithms. Worse still, throw in some uncommon kernel sizes and performance nosedives, no matter the language.

Feedback Loops: CUDA Shines, TVM IR Tanks

Now, here's where it gets wild. In feedback-loop kernel optimization, CUDA takes the crown. It improves with each iteration like a fine wine. TVM IR, however, crashes and burns, showing that optimization sinks when models swim through low-density language.

What's the takeaway? LLMs in code optimization don't thrive on feedback. They lean heavily on their pretrained priors. So, are they truly intelligent, or just parroting pre-learned patterns?

The Bigger Picture: Are LLMs Overhyped?

And just like that, the leaderboard shifts. These findings suggest we might be overestimating LLMs' capabilities in certain tasks. If they're relying on pre-existing patterns rather than dynamic thinking, can they really drive innovation?

This changes the landscape. The labs are scrambling to reconcile these results with the common belief in LLMs' prowess. But here's the burning question: Are LLMs as revolutionary as we think, or are they just glorified memorization machines?

The debate is far from over. But one thing's for sure, LLMs still have a lot to prove before they truly dominate the AI scene.