> The “statistical parrot” assertion is pretty thoroughly disproven by this point
errr... all NNs are just optimisations of an associative probability objective: P(Y|X), they are by definition "statistical parrots". There isn't anything to prove or disprove.
People offering prompts as evidence are people who fundamentally do not understand the basics. NNs aren't strange empirical objects, they're specified by mathematical rules whose properties are known ahead of time.
Any property of a trained NN is derivative of a property of a formula P(Answer|Prompt, TrainingData)
This is an associative statistical relation, which by definition, selects elements of TrainingData by-association with the Prompt.
If the basis by which you understand LLMs is putting prompts into ChatGPT you're severely underqualified for drawing any conclusions about LLMs, and radically subject to confirmation bias.
errr... all NNs are just optimisations of an associative probability objective: P(Y|X), they are by definition "statistical parrots". There isn't anything to prove or disprove.
People offering prompts as evidence are people who fundamentally do not understand the basics. NNs aren't strange empirical objects, they're specified by mathematical rules whose properties are known ahead of time.
Any property of a trained NN is derivative of a property of a formula P(Answer|Prompt, TrainingData)
This is an associative statistical relation, which by definition, selects elements of TrainingData by-association with the Prompt.
If the basis by which you understand LLMs is putting prompts into ChatGPT you're severely underqualified for drawing any conclusions about LLMs, and radically subject to confirmation bias.