Optimal LLM Size

Is bigger always better when it comes to Large Language Models (LLMs)? While the current trend is to create ever-larger models, the practical utility of such enormous datasets is questioned. The focus shifts from sheer size to the specific capabilities required for effective language manipulation. A smaller, more focused model could potentially achieve similar results with a fraction of the computational resources. The ideal LLM wouldn't necessarily possess encyclopedic knowledge, but rather the ability to process and manipulate information effectively, potentially by leveraging external resources.

LLM Size Limit: "is there a theoretical limit to the side i mean is there a law of diminishing returns i assume there would be like how large can the language models get if you just continue to just throw more and more at it does it just get better and better or is eventually just top out"

Desired LLM Capabilities: "i don't actually want a huge language model [...] i want one that can manipulate words [...] i want the smallest possible language model that i can run on my own device that can still do the magic"

Functionality over Size: "it can summarize things and extract facts and generate bits of code"

Questioning the Need for Encyclopedic Knowledge: "is it impossible to summarize text if you don't know that an elephant is large here than a kangaroo because is there something about having that sort of that general not that that common sense knowledge of the world that's crucial if you want to summarize things effectively"

Cost-Benefit Analysis of Larger Models: "if you made a gpt five that was ten times the size of gpt four and cost ten times as much much to run does is that actually really useful"

The drive for increasingly larger LLMs might be overlooking the potential of smaller, specialized models. By focusing on core functionality and leveraging readily available resources, it could be possible to create efficient and cost-effective language models that deliver practical value.