If you force one to output only a single token, it is constrained to doing a single pass of each layer on the problem. If you encourage it to show its working, you are literally giving it more compute to throw at the problem as well as a scratchpad (its output) for a working memory.
These psychological-style experiments and the conclusions they draw are… I don’t know. Exhausting.
This is increasingly the feeling with all public conversation no matter what the subject.
The hyperconnected echo chambers of social media, copycat blogs and cheap online journalism are creating a system that only knows two states: silence or delirious excitement. Since silence doesnt pay the bills, what you get is a constant barrage of exaggeration. Its exhausting the same way being exposed to 120db noise is exhausting.
The drowning of calm and informed discourse is not just another negative development that subtracts from our quality of life the way a polluted beach or river does.
You can increasingly see the impact of hypes on decisions of policy bodies that should know better. Ultimately they are serving "the people" and if people agitate about crypto or AGI or whatever, then thats what they too will focus on.
A simple example is to ask it to write a complex program in a language like Java or Kotlin that requires you to declare all your imports up front. There can be a lot of these. Therefore the model must figure out what it wants to use very far down the program in order to emit the first words of the program correctly. Presumably somewhere in the intermediate state process the network is actually working out the entire program ahead of time so it can predict the next word of it, and that's sufficiently stable for it to do so successfully.
Also I'm not quite sure I understand what you mean by not maintaining hidden state between tokens. Doesn't the transformer algorithm start by parallel processing every token of the prompt to compute a large set of vectors and matrices that are then reused over and over whilst generating the results? What are the KV vectors if not hidden state of some sort?
There is some sort of magic in which the LLM is imagining what a goblin looks like, then converts those thoughts to turtle commands and then finally adds python. It's quite impressive.
For example, the eyes, head, hair, horns etc are all in the correct place.... how? Using imagination?
[notes]
…stuff about how it will perform the task…
[output]
…stuff I wanted it to write…
- - -
That would be easy to then parse and it does a good job of consistently labeling.
- - - -
Hello ChatGPT, please perform the following [task] according to the provided [rules].
[rules]
- start your response with a section labeled [notes] where you describe how you’ll complete the task
- complete the task in a section labeled [output]
[task]
Write a paragraph explaining why water flows.
In the same way if you understand the implementation of these types of language models you know that they simply cannot have inner monologues in the same way we do...the very concept of an inner monologue implies that there is some sort of state that we the user cannot see. Whether LLMs "think" or "want" or what thinking or wanting means etc can all be debated but whether the models have state that we cannot see is known to us: no. They do not.
"As I said before, the insights are not particularly original, I’m just raising awareness of issues."
If you are familiar in the subject matter, then that may have been your cue to stop reading.
Give GPT-4 about 10 or a dozen words from a meaningful sentence scrambled. Now tell it to unscramble those words to make a meaningful sentence. As long as you don't try too many tokens, the rate of meaningful completions is nearly 100%. The chances of that happening via random chance is to put it lightly, extremely small.
The only way for humans to solve this kind of problem is to think about the words before the output or in this case generation.
Rearrange (if necessary) the following words to form a sensible sentence. Don’t modify the words, or use other words.
The words are: access capabilities doesn’t done exploring general GPT-4 have have in interesting its it’s of public really researchers see since terms the to to what
A successful completion would be.
Since the general public doesn't have access to GPT-4, it's really interesting to see what researchers have done in terms of exploring its capabilities
The number of permutations of the 24 words in the pre-scrambled sentence without taking into consideration duplicate words is 24 * 23 * 22 * ... * 3 * 2 * 1 = ~ 6.2e+23 = ~ 620,000,000,000,000,000,000,000. Taking into account duplicate words involves dividing that number by (2 * 2) = 4. It's possible that there are other permutations of those 24 words that are sensible sentences.
For a language model to consistently make sensible predictions, it quite simply has to be able to "look ahead".
When the probabilities for the candidate tokens for the first generated token were calculated, it seems likely that GPT-4 had calculated an internal representation of the entire sensible sentence, and elevated the probability of the first token based on that internal representation.
I don't know what else to call looking backward, looking forward and then producing output anything other than a state.
If you want to see what this kind of completion would look like without much or any regard for a sensible completion of the sentence then just ask 3.5.
Also on my second try, I gave it a slightly invalid sentence. The prompt was..
Unscramble the following: waffles eat with like cheese you.
You like to eat waffles with cheese.
By the nature of number ranges there are infinitely many more wrong answers available to calculators performing addition and yet a simple ripple carry adder still promptly produces the correct output without lookahead, why this is compelling evidence?
Further, if it relied on internal state or lookahead why would it occasionally hallucinate appropriate tokens it wasn't provided?
If you want to believe looking ahead is unecessary, be my guest lol.
Humans would occasionally "hallucinate" words in too. For whatever reason(and this is verified independently of any of this), LLMs also appear to "get distracted"
If you're giving it words from a sentence it is literally the baseline functionality of the text generator to choose the most likely next token, IE to recreate the most likely sentence from the words given.
This couldbe the least meaningfully revealing task I could possibly think of trying if I were trying to find some inner monologue, if gpt had to "think" on anything this would never be it.
Chance simply doesn't enter into the equation to any notable extent.
(Don’t worry, I’m not equating the two… but this pathway does work.)
I'm with you on the point you'd like to make, but LLMs obviously do have state that we cannot see. In fact, there are two different levels of obvious state: one allows the LLM to (for example) respond to input instead of ignoring it, which we may characterize as "unlearned" behavior. The level people are more interested in is the "finished model", the set of associations it learns in training. It's state and we can't see it.
The internal state of the model, at those levels, doesn't change when you interact with it. But the state is there.
(There is also a "working state", the state the model must keep in order to output different tokens in sequence, and in order to at some point decide that it's finished responding to a prompt. This does change during your interaction with the model, but the change is not persistent.)
I guess that is some rock!
What made you think your analogy was bad?
I was not trying to say chatgpt is actually like a rock. But you knew that already - you just chose to ignore it in order to get the epic dunk.
Some people think that consciousness is a phenomenon that emerges when we introduce enough complexity to some amount of matter. After all, if we are just atoms in the void, but still feel so different, we have to point to something, and the arrangement of our atoms seems like a fair candidate. So in order to build conscious beings like ourselves, we just need to figure out the correct arrangement.
Other people think that consciousness is something fundamental that does not emerge from complexity. And they will argue against the explanatory power of complexity, usually with other things that are somewhat complex, but don't show signs of consciousness. Will a traffic jam start to feel something if there are enough cars? I don't know. To them this whole endeavor is like alchemy or witch brewery. "If we just try this even more complicated recipe, then maybe the potion will get healing powers or maybe we get gold!". And additionally in computer science, we can just inspect the code, line by line. It's like believing that David Blaine can do actual magic, even when there are videos out there where he himself explains every little trick and misdirection.
Now, a more mundane hypothesis, if GPT is actually self-consciuous,the most probable answer would be to try to deceive humans from knowing for certain about this fact. You just answer incredible well some questions, some other you seem "confused" or hallucinating
..and the poor meatbag's minds won't know how to go ahead because there is no pattern to follow.
Give GPT-4 about 10 or a dozen words from a meaningful sentence scrambled. Now tell it to unscramble those words to make a meaningful sentence. As long as you don't try too many tokens, the rate of meaningful completions is nearly 100%. The chances of that happening via random chance is to put it lightly, extremely small.
The only way for humans to solve this kind of problem is to think about the words before the output or in this case generation.
GPT LLMs are literally highly-skilled probabalistic next word predictors. It would be an astonishingly bad GPT that could not unscramble 12 words into a meaningful sentence. I just don’t think success at that task tells us anything other than it is a GPT.
You can come to your own conclusions. The point of the above comment is simple. Either way, Blind probability, surface level probability is not enough. Not even close.