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Comment by dizhn | Hacker News Reader

Why would you need chance? It's applying the same parameters/algorithms to the same problem. As far as I understand you have to prompt it to provide more novel answers to the questions and there's some randomness built into the model so it doesn't sound like a broken record. This is is how it's supposed to work. Why do you consider this evidence of state?

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The prompt would basically be (but with less words. Around 10 is the best for consistency from my experiments. Much higher than that and it's hit or miss).

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.

It doesn’t need to look ahead, all of the words are already in the prompt behind the current position, so it just needs to use the existing context to infer the next word

Also on my second try, I gave it a slightly invalid sentence. The prompt was..

    Unscramble the following: waffles eat with like cheese you.

It responded with:

    You like to eat waffles with cheese.

But notice that the prompt doesn’t have the word “to” anywhere in it. But it was the most likely word that was supposed to go there.

Feel free to try and ask the same question with people. A good chunk of them will insert the "to" as well. It's not a gotcha.

This isn't looking ahead. All those words are "behind", since they are part of the prompt. No hidden state is required for this to work.

Isn't the conclusion here just begging the question?

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?

Arithmetic has a number of always applicable steps. Unscrambling does not unless one of those steps is "think".

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"

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