> * When we give the LLM a prompt, it simulates every possible entity consistent with the prompt.
edit to add: this is similar to how people discussing evolutionary biology will often use "evolution wants to..." as shorthand for something like "evolution, which obviously cannot want things due to being a process and not an entity, nevertheless can be accurately modeled as an entity that wants to...". Someone will invariably come along in the comments and say, "Nonsense, how can evolution 'want' anything? You must have failed Bio 101!"
The superposition of possible attitudes is a good one. Even if that's not the way LLMs "actually" work, it's descriptive of the possibility space from our perspective. And the dive into narrative theory + the stickiness of opposites is nice. Narratives have their own momentum in a "stone soup" kind of way - everyone who hears it participates and adds fuel to the fire. Even rejecting the narrative gives it validity in a price anchoring / overton window way.
I agree, and go even further:
models that explain behavior are all we have ever had.
it's all only "models that explain this or that" all the way to the 'bottom'. To suppose we can really directly access the "the real objective truth of what's happening" is to ignore the way in which we connect with the "real objective truth"; the same as fish who ignore the ocean.
to argue about what is really happening is to argue about which words to use to describe what is really happening without noticing the nature of languages/words and frameworks or 'systems of thought' which we are using to argue (and indeed, are arguing about)
all this summed up by this quote from about about the pedagogy programing languages: "Sometimes the truest things can only be said in fiction"
This is just Bayes' rule. The probability of an LLM generating any particluar output is the sum over every possible entity of the probability of seeing that entity multiplied by the chance that entity would generate the output.
If you formulate it like that the prompt is decoupled from the LLM capabilities and can be anything. And if you restrict the prompt to cover only what the LLM understands the sentence becomes trivial.
Train a LLM with ASCII and try to get it to simulate anything that is outside of that (ancient sumerian script for example). If you only input ASCII it can generate every possible output in ASCII, most with very low probability but still.
After writing this, I'm not even sure what 'simulating' means in this context.
For example, the string "1010101010"... could be the output of a function
def generate_char_random(prev_string):
x = random()
if (x > 0.5):
yield(1)
else:
yield(0)
def generate_char_alternating(prev_string):
x = float(prev_string[-1])
if (x < 0.5):
yield(1)
else:
yield(0)
The LLM of course does not contain an infinite list of generative functions and weight their outputs. But to the extent that it works well and compactly approximates Bayesian reasoning, it should approximate a program that does.
Simulating as in, having equivalent (or "similar enough") input-output behavior, I'd assume.
Or are you thinking that it is simulating the aggregated behavior of all humans whose text-outputs are stored on the internet?
Are we saying it is simulating the combined input-output -behavior of all humans whose writings appear on the internet? But does such an "entity" exist and does it have behavior? I write this post and you answer. It is you who answers, not some mythical text-generator-entity that is responsible for all texts on the internet. There is no such entity is there?
It does not make sense to say that we are simulating the behavior of some non-existent entity. Non-existent entities do not have behavior, therefore we can not simulate them.
(Just speaking hypothetically here).
While we understand LLMs, we don't understand the human brain, and in particular I don't think we've yet proven that human brains don't contain embedded routines that are similar to LLMs.
Someone with your particular writing style might be one, of several, simulations that are approximated within the LLM. Just like I can have it respond in the style of Spock from Star Trek.
To produce something that resembles the output of the fictional character Spock is straightforward, just take the texts that are parts of the fiction where fictional Spock speaks, and reassemble then using probabilities that can be calculated by statistically analyzing those texts. That is what LLMs are doing, right? And results can be quite surprising. I assume people were similarly impressed when they first saw movies.
But LLMs are not simulating anything, just like a movie or a photograph are not simulating anything, even though they may PROJECT the visual appearance of their subjects.
Are movies AI? I think it is clear to us they are not even though the characters on the screen seem to behave very intelligently. Movies are about representing and portraying the appearance of real or fictional events in the world. Similarly LLMs are about portraying texts on the internet. LLMs in my opinion are more like interactive movies than simulations of intelligence.
I do believe "true AI" will come eventually, and LLMs can give us an impression of what it might look like when it arrives, just like movies can give us an impression of Spock, who doesn't exist.
And, I'd argue, so can many of us humans! After reading a Jane Austen novel, it can take a conscious effort not to write in the style of Austen. ChatGPT manages it better than I do. I don't think I know her well enough to get into her brain, but it seems like there's something like a transfer function called STYLE between "the message Jane Austen wants to write" and "the words Jane Austen chooses to write".
_____
intended message --> |STYLE| --> selected words
|_____|
I don't think ChatGPT can simulate Jane Austen well enough to tell us her opinions about her childhood or any other message that she might have generated, but it seems to be able to replicate very closely the steps that Jane Austen's own mind herself was following as part of that STYLE.
ChatGPT does seem to go even further than this, because it also has some understanding of where different sorts of characters would steer the message of a conversation. But while it's believable, it's hard to say how accurate that is to what any particular real person would say.
But IMITATING the output of something is not the same as SIMULATING the process that produces that output.
Taking a photograph or creating a movie imitates the reality around us. It does not simulate the processes that produce the look and feel of our reality.
The harder it is to discriminate between A and B on a long series of diverse inputs, the more likely it is that A and B are internally equivalent, not just externally similar. The reason is that there's no better fit than B = A.
I'm increasingly doubting whether my own brain might not, internally, use something that is architecturally similar to an LLM in order to compose comments like the one I'm writing now.
It is possible to repeat words and sentences without having any idea of what they mean. I think the LLMs are currently at that stage.
This has an interesting core with a whiff of bullshit.