I don’t think this line of argumentation is actually challenging the concept of stochastic parroting on a fundamental level. The ability of generative ML to create images or solve math problems or engage in speculation about stories, etc, were all known to the researchers who coined the term; these things you point to, far from challenging the concept of stochastic parrots, are assumed to be true by these researchers.
When you point to these models not understanding how reciprocal relationships between objects work, but apologize for it by reference to its ability to explain who Tom Cruise’s mother is, I think you miss an opportunity to unpack that. If we imagine LLMs as stochastic parrots, this is a textbook example: the LLM cannot make a very basic inference when presented with novel information. It only gets this “right” when you ask it about something that’s already been written about in its training data many times: a celebrity’s mother.
The model is very excellent at reproducing reasoning that it has been shown examples of: Tom Cruise has a mother, so we can reason that his mother has son named Tom Cruise. For your sound example, there is information about how sound propagation works on the internet for the model to draw on. But could the LLM speculate on some entirely new type of physics problem that hasn’t been written about before and fed into its model? How far can the model move laterally into entirely new types of reasoning before it starts spewing gibberish or repeating known facts?
You could fix a lot of these problems. I have no doubt that at some point they’ll work out how to get ChatGPT to understand these reciprocal relationships. But the point of that critique isn’t to celebrate a failure of the model and say it can never be fixed, the point is to look at these edge cases to help understand what’s going on under the hood: the model is replicating reasoning it’s seen before, and yes, that’s impressive, but it cannot reliably employ reasoning to truly novel problem types because it is not reasoning. You may not find that troubling, and that’s your prerogative, truly, but I do think it would be useful for you to grapple with the idea that your arguments are compatible with the stochastic parrots concept, not a challenge to them.
I don’t think this line of argumentation is actually challenging the concept of stochastic parroting on a fundamental level. The ability of generative ML to create images or solve math problems or engage in speculation about stories, etc, were all known to the researchers who coined the term; these things you point to, far from challenging the concept of stochastic parrots, are assumed to be true by these researchers.
When you point to these models not understanding how reciprocal relationships between objects work, but apologize for it by reference to its ability to explain who Tom Cruise’s mother is, I think you miss an opportunity to unpack that. If we imagine LLMs as stochastic parrots, this is a textbook example: the LLM cannot make a very basic inference when presented with novel information. It only gets this “right” when you ask it about something that’s already been written about in its training data many times: a celebrity’s mother.
The model is very excellent at reproducing reasoning that it has been shown examples of: Tom Cruise has a mother, so we can reason that his mother has son named Tom Cruise. For your sound example, there is information about how sound propagation works on the internet for the model to draw on. But could the LLM speculate on some entirely new type of physics problem that hasn’t been written about before and fed into its model? How far can the model move laterally into entirely new types of reasoning before it starts spewing gibberish or repeating known facts?
You could fix a lot of these problems. I have no doubt that at some point they’ll work out how to get ChatGPT to understand these reciprocal relationships. But the point of that critique isn’t to celebrate a failure of the model and say it can never be fixed, the point is to look at these edge cases to help understand what’s going on under the hood: the model is replicating reasoning it’s seen before, and yes, that’s impressive, but it cannot reliably employ reasoning to truly novel problem types because it is not reasoning. You may not find that troubling, and that’s your prerogative, truly, but I do think it would be useful for you to grapple with the idea that your arguments are compatible with the stochastic parrots concept, not a challenge to them.