I have trouble framing this thing in my mind because I do not understand what the distribution is relative to. In the strictest sense, the distribution of internet text is the internet text itself, and everything GPT outputs is an error. In a broad sense, what is an error and what isn’t? I think there’s something meaningful here, but I can not pinpoint it clearly.
This strongly shows that GPT won’t be able to stay coherent with some initial state, which was already clear from it being autoregressive. It only weakly indicates that GPT won’t learn, somewhere in its weights, the correct schemes to play chess, which could then be somehow elicited.
How does this not apply to humans? It seems to me we humans do have a finite context window, within which we can interact with a permanent associative memory system to stay coherent on a longer term. The next obvious step with LLMs is introducing tokens that represent actions and have it interact with other subsystems or the external world, like many people are trying to do (e.g., PaLM-e). If this direction of improvement pans out, I would argue that LLMs leading to these “augmented LLMs” would not count as “LLMs being doomed”.
3a) It applies to humans, and humans are doomed :)
LLMs are already somewhat able to generate dialogues where they err and then correct in a systematic way (e.g., reflexion). If there really was the need to create large datasets with err-and-correct-text, I do not exclude they could be generated with the assistance of existing LLMs.
This strongly shows that GPT won’t be able to stay coherent with some initial state, which was already clear from it being autoregressive
This problem is not coming from the autoregressive part, if the dataset GPT was trained on contained a lot of examples of GPT making mistakes and then being corrected, it would be able to stay coherent for a long time (once it starts to make small deviations, it would immediately correct them because those small deviations were in the dataset, making it stable). This doesn’t apply to humans because humans don’t produce their actions by trying to copy some other agent, they learn their policy through interaction with the environment. So it’s not that a system in general is unable to stay coherent for long, but only those systems trained by pure imitation that aren’t able to do so.
Ok, now I understand better and I agree with this point, it’s like when you learn something faster if a teacher lets you try in small steps and corrects your errors at a granular level instead of leaving you alone in front of a large task you blankly stare at.
I have trouble framing this thing in my mind because I do not understand what the distribution is relative to. In the strictest sense, the distribution of internet text is the internet text itself, and everything GPT outputs is an error. In a broad sense, what is an error and what isn’t? I think there’s something meaningful here, but I can not pinpoint it clearly.
This strongly shows that GPT won’t be able to stay coherent with some initial state, which was already clear from it being autoregressive. It only weakly indicates that GPT won’t learn, somewhere in its weights, the correct schemes to play chess, which could then be somehow elicited.
How does this not apply to humans? It seems to me we humans do have a finite context window, within which we can interact with a permanent associative memory system to stay coherent on a longer term. The next obvious step with LLMs is introducing tokens that represent actions and have it interact with other subsystems or the external world, like many people are trying to do (e.g., PaLM-e). If this direction of improvement pans out, I would argue that LLMs leading to these “augmented LLMs” would not count as “LLMs being doomed”.
3a) It applies to humans, and humans are doomed :)
LLMs are already somewhat able to generate dialogues where they err and then correct in a systematic way (e.g., reflexion). If there really was the need to create large datasets with err-and-correct-text, I do not exclude they could be generated with the assistance of existing LLMs.
This problem is not coming from the autoregressive part, if the dataset GPT was trained on contained a lot of examples of GPT making mistakes and then being corrected, it would be able to stay coherent for a long time (once it starts to make small deviations, it would immediately correct them because those small deviations were in the dataset, making it stable). This doesn’t apply to humans because humans don’t produce their actions by trying to copy some other agent, they learn their policy through interaction with the environment. So it’s not that a system in general is unable to stay coherent for long, but only those systems trained by pure imitation that aren’t able to do so.
Ok, now I understand better and I agree with this point, it’s like when you learn something faster if a teacher lets you try in small steps and corrects your errors at a granular level instead of leaving you alone in front of a large task you blankly stare at.
For a response to this, see my comment above.