limpbizkit4prez

limpbizkit4prez t1_jai7l96 wrote

Reply to comment by _Arsenie_Boca_ in [R] EvoPrompting: Language models can create novel and effective deep neural architectures. These architectures are also able to outperform those designed by human experts (with few-shot prompting) by MysteryInc152

It matters because the authors continue to increase model capacity to do better on a single task and that's it. They also determined that strategy, not the LLM. It would be way cooler if they constrained the problem to roughly the same number of parameters and showed generalization across multiple tasks. Again, it's neat, just not innovative or sexy.

2

limpbizkit4prez t1_jahhmhd wrote

Reply to comment by MysteryInc152 in [R] EvoPrompting: Language models can create novel and effective deep neural architectures. These architectures are also able to outperform those designed by human experts (with few-shot prompting) by MysteryInc152

Lol, I strongly disagree. There are already methods out there that provide architecture design. This is a "that's neat" type of project, but I'd be really disappointed to see this anywhere other than arxiv.

3

limpbizkit4prez t1_j9h3nbm wrote

Reply to comment by blueSGL in [R] ChatGPT for Robotics: Design Principles and Model Abilities by CheapBreakfast9

If you don't know how to code, then regardless of how you interface it's going to be difficult to execute. If you do know how to code, then you'll probably want better encapsulation. I guess what I'm most curious about is if those code examples they give in their paper are able to be ran, like are those libraries that easy to use

3