it just predicts the next word out of likely candidates based on the previous words
An entity that can consistently predict the next word of any conversation, book, news article with extremely high accuracy is quite literally a god because it can effectively predict the future. So it is not surprising to me that GPT’s performance is not consistent.
It won’t even know it’s written itself into a corner
It many cases it does. For example, if GPT gives you a wrong answer, you can often just send an empty message (single space) and GPT will say something like: “Looks like my previous answer was incorrect, let me try again: blah blah blah”.
And until we get a new approach to LLM’s, we can only improve it by adding more training data and more layers allowing it to pick out more subtle patterns in larger amounts of data.
This says nothing. You are effectively saying: “Until we can find a new approach, we can only expand on the existing approach” which is obvious.
But new approaches come all the time! Advances in tokenization come all the time. Every week there is a new paper with a new model architecture. We are not stuck in some sort of hole.
An entity that can consistently predict the next word of any conversation, book, news article with extremely high accuracy is quite literally a god because it can effectively predict the future
I think you’re reading something there other than what I said. Look, today’s LLM’s ingest a ton of text - more accurately tokens - and builds up statistics of which tokens it sees in that context. So statistically if you see the sentence "A nice cup of " statistically the next word is maybe 48% coffee, 28% tea, 17% water and so on. If earlier in the text it says something about heating a cup of oil, that will have a muuch higher chance. It then picks one of the top tokens at (weighted) random, and then the text (array of tokens) is fed in again into the LLM and a new prediction is made. And so on it continues until you stop the loop (usually from a end token or a keyword you’re looking for). Larger LLM’s are better at spotting more subtle patterns - or more accurate it got more layers of statistics that’s applied - but it still has the fundamental issue of going one token at a time and just going by what’s most likely to be the next token.
It many cases it does. For example, if GPT gives you a wrong answer, you can often just send an empty message (single space) and GPT will say something like: “Looks like my previous answer was incorrect, let me try again: blah blah blah”.
Have you tried that when it’s correct too? And in that case you mention it has a clean break and then start anew with token generation, allowing it to go a different path. You can see it more clearly experimenting with local LLM’s that have fewer layers to maintain the illusion.
This says nothing. You are effectively saying: “Until we can find a new approach, we can only expand on the existing approach” which is obvious.
But new approaches come all the time! Advances in tokenization come all the time. Every week there is a new paper with a new model architecture. We are not stuck in some sort of hole.
We’re trying to make a flying machine by improving pogo sticks. No matter how well you design the pogo stick and the spring, it will not be a flying machine.
The issue here is that you are describing the goal of LLMs, not how they actually work. The goal of an LLM is to pick the next most likely token. However, it cannot achieve this via rudimentary statistics alone because the model simply does not have enough parameters to memorize which token is more likely to go next in all cases. So yes, the model “builds up statistics of which tokens it sees in which contexts” but it does so by building it’s own internal data structures and organization systems which are complete black boxes.
Also, going “one token at a time” is only a “limitation” because LLMs are not accurate enough. If LLMs were more accurate, then generating “one token at a time” would not be an issue because the LLM would never need to backtrack.
And this limitation only exists because there isn’t much research into LLMs backtracking yet! For example, you could give LLMs a “backspace” token: https://news.ycombinator.com/item?id=36425375
Have you tried that when it’s correct too? And in that case you mention it has a clean break and then start anew with token generation, allowing it to go a different path. You can see it more clearly experimenting with local LLM’s that have fewer layers to maintain the illusion.
If it’s correct, then it gives a variety of responses. The space token effectively just makes it reflect on the conversation.
We’re trying to make a flying machine by improving pogo sticks. No matter how well you design the pogo stick and the spring, it will not be a flying machine.
To be clear, I do not believe LLMs are the future. But I do believe that they show us that AI research is on the right track.
Building a pogo stick is essential to building a flying machine. By building a pogo stick, you learn so much about physics. Over time, you replace the spring with some gunpowder to get a mortar. You shape the gunpowder into a tube to get a model rocket and discover the pendulum rocket fallacy. And finally, instead of gunpowder, you use liquid fuel and you get a rocket that can go into space.
The issue here is that you are describing the goal of LLMs, not how they actually work.
No, I am describing how they actually work.
it cannot achieve this via rudimentary statistics alone because the model simply does not have enough parameters to memorize which token is more likely to go next in all cases.
True, hence the limitations. That would require infinite storage and infinite compute capability.
Also, going “one token at a time” is only a “limitation” because LLMs are not accurate enough.
No, it’s done because one letter at a time is too slow. Tokens are a “happy” medium tradeoff.
The space token effectively just makes it reflect on the conversation.
It makes a “break” of the block, which lets it start a new answer instead of continuing on the previous. How it reacts to that depends on the fine tune and filters before the data hits the LLM.
To be clear, I do not believe LLMs are the future.
I have just said that LLM’s we have today can’t fix the problems with false data and hallucinations, because it’s a core principle of how it operates. It will require a new approach.
You could add a rocket engine and wings to a pogo stick, but then it’s no longer a pogo stick but an airplane with a weird landing gear. Today’s LLM’s could give us hints to how to make a better AI, but that would be a different thing than today’s LLM’s. From what has been leaked from OpenAI GPT4 has scaling issues so they use mixture of experts. Just throwing hardware at it is already showing diminishing returns. And we’re learning fascinating new ways of training them, but the inherent problem is the same.
For example, if you ask an LLM if it can give an answer to a question, it will have two paths to go down, positive and negative. Note, at the point where it chooses that it doesn’t know how to finish it, it doesn’t look ahead. But it sees for example that 80% of the answers in the texts it’s been trained on starts with a positive, then it will most likely start with “yes” - and when it does that it will continue to generate an answer - often very convincing and plausibly real looking answer, because it already committed to that path.
And as for the link about teaching it backspace token, the comments there are already pointing out the issue:
It’s interesting that in the examples (Table 3 on page 21), the model uses the backspace token to erase the randomly-added token from the prompt, but it does not seem to ever use the token to correct its own output. I’m curious how frequently the model actually uses this backspace token in practice - and if the answer is “vanishingly rarely”, what is the source of the improved Mauve score and sample diversity they show? Is it just that the different training procedure gives an improvement?
For it to use the backspace, wouldn’t it have to predict the wrong token with greater confidence than the corrected token? I would think this would require more examples of a wrong token + correction than the correct token, which seems a bit odd.
Almost none of the text it’s trained on has a backspace token, and to finetune it in is tricky since it’s a completely new concept - and remember it’s still doing token for token - so it would have to write a token and then right after find out that it’s more likely to send a backspace token than to continue it. It’s interesting, and LLM’s can pick up on some crazy patterns, but I’m skeptical.
