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That indeed seems like the kind of compare and contrast text that LLMs often produce. The format is something like "look at the commonly described characteristi

That indeed seems like the kind of compare and contrast text that LLMs often produce. The format is something like "look at the commonly described characteristics of X and Y, draw between them and describe the commonly described contrasts between the respective characteristics". It's rather tedious and often only useful to learn those characteristics. Especially the way it often falls down is that it can't extend-by-analogy the various equivalent characteristic. Or it doesn't get the idea that our question is "how could we make X work like Y" and can't really answer it if it did get it. In this example, as far as I can tell, the relevance of RFT would come if the transformers architecture of LLMs, which allows output based on context, allowed these models to partly simulate the "personal histories or cultural contexts" through having on language reflecting those things. I don't know if that's a reasonable claim but it seems like LLM can't really "get" even that sort of possibility.

turnsout 3y ago
Some further support for RFT as a useful model might be LLM's ability to take on different "voices" or perspectives. For example, you can ask a big LLM to argue that cake is the building block of civilization from the perspective of an excitable birthday cake, and then argue against this theory from the perspective of an archeologist. Despite never having "seen" an argument about cake archaeology, it can readily come up with both sides. To me, this goes beyond "stochastic parrot," and implies that the LLM is actually modeling bidirectional frames. That would be fascinating, because it would be further evidence that language and complex thought are inextricably linked.
- joe_the_user 3y ago
  A more prosaic explanation of the birthday cake text is that the transformers architecture result in a system which returns learned information transformed, mapped according various context tokens. The system learns "X being the basis of civilization" type arguments and learns maps of those arguments according to various things including "from Y's point of view". One thing that I've noticed that ChatGPT can't do, not at all, is "write a show rather than tell paragraph". For example, writing a paragraph describing a man walking down the street in such way that some qualities (old, tired, etc) are evident without being directly stated (For example: "He stepped slowly and carefully unto the street, using his cane to steady himself" etc). Because the thing is ultimately transforming language. Oppositely, that these system can still do a lot is naturally an indication that a lot of what's being demonstrated are "just" structures in language - though if the structures are quite, the "just" part is quite relative.

turnsout 3y ago

Some further support for RFT as a useful model might be LLM's ability to take on different "voices" or perspectives. For example, you can ask a big LLM to argue that cake is the building block of civilization from the perspective of an excitable birthday cake, and then argue against this theory from the perspective of an archeologist. Despite never having "seen" an argument about cake archaeology, it can readily come up with both sides. To me, this goes beyond "stochastic parrot," and implies that the LLM is actually modeling bidirectional frames. That would be fascinating, because it would be further evidence that language and complex thought are inextricably linked.

joe_the_user 3y ago
A more prosaic explanation of the birthday cake text is that the transformers architecture result in a system which returns learned information transformed, mapped according various context tokens. The system learns "X being the basis of civilization" type arguments and learns maps of those arguments according to various things including "from Y's point of view". One thing that I've noticed that ChatGPT can't do, not at all, is "write a show rather than tell paragraph". For example, writing a paragraph describing a man walking down the street in such way that some qualities (old, tired, etc) are evident without being directly stated (For example: "He stepped slowly and carefully unto the street, using his cane to steady himself" etc). Because the thing is ultimately transforming language. Oppositely, that these system can still do a lot is naturally an indication that a lot of what's being demonstrated are "just" structures in language - though if the structures are quite, the "just" part is quite relative.