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.
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 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”.
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.
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.
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.
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.