Are LLMs Hitting a Plateau?
Not So Fast
Large Language Models (LLMs), or generative AI in general, have undeniably revolutionized the field of artificial intelligence — with rapid advancements leading to powerful applications across various industries.
We all know about ChatGPT, Gemini, Claude, and whatnot at this point, right? And tech geeks like me know about open-source models as well, like Llama, Mistral, Phi, etc…
However, recent “progress/returns” indicate that these models may be reaching a plateau in their development.
While it’s true that progress in any technology eventually slows down, I think it’s premature to declare that LLMs have hit their peak.
Why do we feel that LLMs have plateaued?
Diminishing Returns
As LLMs grow larger and more complex, the improvements in performance for each additional parameter or training data point become less pronounced.
This doesn’t mean progress has stopped, but rather that optimization is becoming more challenging.