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. They are as smart, and as fallible, as wikipedia + reddit + twitter, etc, etc. They are as fallible as their constructing dataset.
Think about how ice cream sales correlate with drownings. There is no direct causality, but that won’t stop an LLM from seeing the pattern or implying causality, because it has no real intelligence and doesn’t know any better.
“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. (which right now sometimes requires weird phrases, etc.)
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, and then use that as an input to the next stage of tweaking the prompt.
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 our phones can run basic models and they do. Some phones use a basic LLM for keyboard auto correct for example.
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.
I really wish all of these companies racing to replace their existing software features and employees with LLMs understood this.
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 only used the LLM understand an obscure and deliberately confusing chapter of the Hitchhiker’s Guide book, to write and execute this python script.
# To calculate six by nine in base 13, we multiply the numbers in our standard decimal system and then convert the result to base 13.# Calculate 6 * 9 in decimal
result_decimal = 6 * 9# Convert the result to base 13# The easiest approach is to use the divmod() function repeatedly to get the remainder (which corresponds to the base 13 digit) # and update the quotient for the next iteration until the quotient is 0.defdecimal_to_base_n(num, base):
if num == 0:
return"0"
digits = []
while num:
num, remainder = divmod(num, base)
digits.append(str(remainder))
return''.join(digits[::-1])
# Convert the decimal result to base 13
result_base_13 = decimal_to_base_n(result_decimal, 13)
result_base_13
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.
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.
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 uniquely associated with adaptive complex systems.
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.
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. They are as smart, and as fallible, as wikipedia + reddit + twitter, etc, etc. They are as fallible as their constructing dataset.
Think about how ice cream sales correlate with drownings. There is no direct causality, but that won’t stop an LLM from seeing the pattern or implying causality, because it has no real intelligence and doesn’t know any better.
“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.
Prompt engineering is about expressing your intent in a way that causes an LLM to come to the desired result. (which right now sometimes requires weird phrases, etc.)
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, and then use that as an input to the next stage of tweaking the prompt.
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 our phones can run basic models and they do. Some phones use a basic LLM for keyboard auto correct for example.
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.
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 only used the LLM understand an obscure and deliberately confusing chapter of the Hitchhiker’s Guide book, to write and execute this python script.
# To calculate six by nine in base 13, we multiply the numbers in our standard decimal system and then convert the result to base 13. # Calculate 6 * 9 in decimal result_decimal = 6 * 9 # Convert the result to base 13 # The easiest approach is to use the divmod() function repeatedly to get the remainder (which corresponds to the base 13 digit) # and update the quotient for the next iteration until the quotient is 0. def decimal_to_base_n(num, base): if num == 0: return "0" digits = [] while num: num, remainder = divmod(num, base) digits.append(str(remainder)) return ''.join(digits[::-1]) # Convert the decimal result to base 13 result_base_13 = decimal_to_base_n(result_decimal, 13) result_base_13Machine 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.
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.
Congrats. You don’t understand the difference between a statistical model and a human.
I expected more from a gaylord fartmaster. 2/10.
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”?
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 uniquely associated with adaptive complex systems.
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.