Congrats. You don’t understand the difference between a statistical model and a human.
Comment on AI Prompt Engineering Is Dead
gaylord_fartmaster@lemmy.world 3 months agoMachine learning could find those strengths and weaknesses and learn to work around them likely better than a human could. It’s just trial and error. There’s nothing about the human brain that makes it better suited to understanding the inner logic of an LLM.
WhatAmLemmy@lemmy.world 3 months ago
gaylord_fartmaster@lemmy.world 3 months ago
In what way?
Why couldn’t even a basic reinforcement learning model be used to brute force “figure out what input gives desired X output”?
WhatAmLemmy@lemmy.world 3 months ago
Because the training data is man made so will never be 100% accurate, and critical thought is required to set the desired output, and understand if the output makes sense?
Statistical models find patterns in one’s and zeros. They don’t apply critical thought.
jacksilver@lemmy.world 3 months ago
Actually most (I think all, but not 99% positive) machine learning models are incapable of doing straight arithmetic. Due to the way they are built ML models, including deep learning models, can only learn relationships in a limited input space.
This is most apparent when you test LLMs on different arithmetic operations:
- For addition, it does okay up until you get to millions or billions
- Multiplication I think breaks at the 100/1000 level
- exponents almost break immediately
- Give it decimal values and it also breaks relatively quickly for any operation.
This has to do with the fact that LLMs are effectively multiple layers of linear functions, so higher order operations break down faster.
sudoreboot@slrpnk.net 3 months ago
For that you need a program to judge the quality of output given some input. If we had that, LLMs could just improve themselves directly, bypassing any need for prompt engineering in the first place.
The reason prompt engineering is a thing is that people know what is expected and desired output and what isn’t, and can adapt their interactions with the tool accordingly, a trait associated to adaptive complex systems.
corsicanguppy@lemmy.ca 3 months ago
If we could have programmed around this prior, then people who can and can’t Google wouldn’t be a thing: Google would just know what results to bring up without the search-curse-refine-repeat cycle. Prompt engineering seems like an extension of Google search-fu.
gaylord_fartmaster@lemmy.world 3 months ago
Yep, exactly, and it’s been studied and put in to practice effectively already.
Prompt tuning is not the only way to fine tune the output of an LLM, and since the goal for most is going to be to make them usable by anyone, that’s going to be the least desirable route.
sudoreboot@slrpnk.net 3 months ago
I know LLMs are used to grade LLMs. That isn’t solving the problem, it’s just better than nothing because there are no alternatives. There aren’t enough humans willing to endlessly sit and grade LLM responses.
realharo@lemm.ee 3 months ago
Yes there are, in addition to the thumbs up/down buttons that most people don’t use, you can also score based on metrics like “did the person try to rephrase the same question again?” (indication of a bad response), etc.