simply ask the word generator machine to generate better words, smh
this is actually the most laughable/annoying thing to me. it betrays such a comprehensive lack of understanding of what LLMs do and what “prompting” even is. you’re not giving instructions to an agent, you are feeding a list of words to prefix to the output of a word predictor
in my personal experiments with offline models, using something like “below is a transcript of a chat log with XYZ” as a prompt instead of “You are XYZ” immediately gives much better results. not good results, but better
Our brains only emulate precision as well. We’re better at it not an an architectural level but just because we’re configured to use various strategies to check thoughts against other thoughts and observations.
We can’t directly perceive logic. We have heuristics for generating logical steps, and we have heuristics for locating and detection obvious breaks. But nobody has an algorithm in their own head to rigorously check all the logic using a single pass through some kind of structure. It’s an asymptotic thing we approach by sort of slashing at logical claims from various angles to see if we can break the structure. We have a set number of slashes we’re sort of biased toward being satisfied with that “huh, the logic is sound” on that one.
I think LLMs could be a lot more precise, without much change in the architecture of the neural net parts, if we just did some old school code (or for fun we could use natural language interpreted like this set of leaked prompt instructions) and the code carries out a strategy of checking A versus B and having different pairs of LLMs debate with each other and have an LLM boss a different one around by writing out lists like “Consider the part with the table. Is there any way that could go wrong?”
Or, even better, recognize that it relies on losing the larger prompt context.
Instead of “now go back and review your idea for problems”, you present it to a fresh LLM without knowledge of why it’s being asked:
Does this plan: yadda-yadda, make sense in terms of the sequence of events? If anything is out of order, report bad plan. Else report okay.
A different LLM is asked:
Does this plan: yadda-yadda, make sense in terms of the cash flows in and out? Do they add up? (If there isn’t any money involved in the plan you report it as okay)
Yet another:
Does this plan: yadda-yadda-yadda, make sense in terms of the first step not having any other requirements that aren’t already true?
… etc
Then another LLM is being presented with:
Here’s what seventeen different LLMs said about whether this plan makes sense on various dimensions. Your response should just be whether any items on this list read “not okay”:
Sequence: okay
Cash flow: okay
First step immediately doable: not okay
Each step actually required for step right after it: okay
…
And it builds up from there.
You can also have LLMs define these structures of pipelines of things to check and all this in order to pass an idea as legit. You can even just copy each exact prompt-check multiple times in parallel and average those outputs to eliminate noise ephemera.
Main point: maybe human logical or design precision comes from being able to do the equivalent of context-free presentation of sub-questions to little LLMs in the mind. To divorce a particular evaluation from the bias introduced by narrative-generation in context.
comprehensive lack of understanding of what LLMs do and what “prompting” even is. you’re not giving instructions to an agent, you are feeding a list of words to prefix to the output of a word predictor
Why not both? Like, a mouse is nothing but chemical reactions but a mouse is also an intelligent thing. A house is just wood and plaster but it’s also a home. A letter is just ink on wood fibers but it’s also a job offer.
An LLM is nothing but predictive text generator / statistical prompt completer / glorified autocomplete / an array of matrices of floating point numbers / a csv file.
But it’s also a personlike mind that thinks and follows instructions simply because the following of instructions was a behavior manifest in the set of utterances it was shaped around.
Happy to break any of these seemingly woo words down into precise engineering definitions if you need, but please trust I’m
using them because they’re the shortest way to convey legit concepts when I say:
The trained model has absorbed the spirit of those whose speech it trained on. That spirit is what responds to instructions like a person, and which responds to being addressed as “You”.
simply ask the word generator machine to generate better words, smh
this is actually the most laughable/annoying thing to me. it betrays such a comprehensive lack of understanding of what LLMs do and what “prompting” even is. you’re not giving instructions to an agent, you are feeding a list of words to prefix to the output of a word predictor
in my personal experiments with offline models, using something like “below is a transcript of a chat log with XYZ” as a prompt instead of “You are XYZ” immediately gives much better results. not good results, but better
it’s all so anti-precision
Our brains only emulate precision as well. We’re better at it not an an architectural level but just because we’re configured to use various strategies to check thoughts against other thoughts and observations.
We can’t directly perceive logic. We have heuristics for generating logical steps, and we have heuristics for locating and detection obvious breaks. But nobody has an algorithm in their own head to rigorously check all the logic using a single pass through some kind of structure. It’s an asymptotic thing we approach by sort of slashing at logical claims from various angles to see if we can break the structure. We have a set number of slashes we’re sort of biased toward being satisfied with that “huh, the logic is sound” on that one.
I think LLMs could be a lot more precise, without much change in the architecture of the neural net parts, if we just did some old school code (or for fun we could use natural language interpreted like this set of leaked prompt instructions) and the code carries out a strategy of checking A versus B and having different pairs of LLMs debate with each other and have an LLM boss a different one around by writing out lists like “Consider the part with the table. Is there any way that could go wrong?”
Or, even better, recognize that it relies on losing the larger prompt context.
Instead of “now go back and review your idea for problems”, you present it to a fresh LLM without knowledge of why it’s being asked:
A different LLM is asked:
Yet another:
… etc
Then another LLM is being presented with:
And it builds up from there.
You can also have LLMs define these structures of pipelines of things to check and all this in order to pass an idea as legit. You can even just copy each exact prompt-check multiple times in parallel and average those outputs to eliminate noise ephemera.
Main point: maybe human logical or design precision comes from being able to do the equivalent of context-free presentation of sub-questions to little LLMs in the mind. To divorce a particular evaluation from the bias introduced by narrative-generation in context.
Butterfly man: “Is this recursive self-improvement”
Why not both? Like, a mouse is nothing but chemical reactions but a mouse is also an intelligent thing. A house is just wood and plaster but it’s also a home. A letter is just ink on wood fibers but it’s also a job offer.
An LLM is nothing but predictive text generator / statistical prompt completer / glorified autocomplete / an array of matrices of floating point numbers / a csv file.
But it’s also a personlike mind that thinks and follows instructions simply because the following of instructions was a behavior manifest in the set of utterances it was shaped around.
Happy to break any of these seemingly woo words down into precise engineering definitions if you need, but please trust I’m using them because they’re the shortest way to convey legit concepts when I say:
The trained model has absorbed the spirit of those whose speech it trained on. That spirit is what responds to instructions like a person, and which responds to being addressed as “You”.
That’s why addressing it as “you” works at all.