Comment on AI Prompt Engineering Is Dead
WhatAmLemmy@lemmy.world 3 months ago
This is dumb. Literally nothing has changed. Anyone who knows anything about LLM’s knows that they’ve struggled with math more than almost every other discipline. It sounds counter intuitive for a computer to be shit at math, but this is because LLM’s “intelligence” is through mimicry. They do not calculate math like a calculator. They calculate all responses based on a probability distribution constructed from billions of human text inputs. E.g. When input equal “YHXURUAG”, the most probable response based on MY SPECIFIC INPUT DATASET OF X BILLION WORDS is “GOPALEFDT”. They are as smart, and as fallible, as wikipedia + reddit + twitter, etc, etc. They are as fallible as their constructing dataset.
“Prompt engineering” is about understanding an LLM’s strengths and weaknesses, and learning how to work with them to build out a context and efficiently achieve an end result, whatever that desired result may be. It’s not dead, and it’s not going anywhere as long as LLM’s exist.
It’s not dead, and it’s not going anywhere as long as LLM’s exist.
Prompt engineering is about expressing your intent in a way that causes an LLM to come to the desired result.
It will go away as soon as LLMs get good at inferring intent. It might not be a single model, it may require some extra steps, etc., but there is nothing uniquely “human” about writing prompts.
Future systems could for example start asking questions more often, to clarify your intent better.
Future systems could for example start asking questions more often
Current systems already do that. But they’re expensive and it might be cheaper to have a human do it. Prompt engineering is very much a thing if you’re working with high performance low memory consumption language models. We’re a long way from having smartphones with a couple terabytes of RAM and a few thousand GPU cores… but we do have primitive models right now which can run on a phone (iPhones use one to figure out what key a user intended to press when their fat thumb hits six keys at the same time).
You know, I had gotten frustrated using it because it wouldn’t understand me, but now I’ll use the approach to find out how it understands me
Machine 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.
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.
can adapt their interactions with the tool accordingly
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.
If we had that, LLMs could just improve themselves directly, bypassing any need for prompt engineering in the first place.
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.
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.
Congrats. You don’t understand the difference between a statistical model and a human.
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”?
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.
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:
This has to do with the fact that LLMs are effectively multiple layers of linear functions, so higher order operations break down faster.
If input.type = int, use calculator.
I mean it’s not really like humans are good at math either, we are good at making abstractions and following linear rules but we are slow and fallible. Digital computation is just near absolute the best method for doing math. LLMs are decent abstraction and general problem solving tho. They are not as creative as people but they are still pretty good! It’s a step on the right direction for true agi. Honestly even when we have agi I doubt they will ever beat raw cpus in computation speed.
Re math, enter functionary-able models
chetradley@lemmy.world 3 months ago
I really wish all of these companies racing to replace their existing software features and employees with LLMs understood this. So many applications are dependent on a response being 100% accurate for a very specific request as opposed to being 80% accurate for a wide variety of requests. “Based on training data, here’s what a response to your input might look like” is pretty good for conversational language and image generation, but it sucks for anything requiring computation or expertise. Worst of all, it’s so confidently wrong about things I might as well be back on Reddit.
abhibeckert@lemmy.world 3 months ago
They totally understand it. And OpenAI has solved it. For example while researching The Ultimate Answer to Life the Universe and Everything, I asked it to calculate 6 by 9 in base 13 and got the correct answer - 42. ChatGPT didn’t use the LLM to calculate that, it used the LLM to write and execute the following python script: