> That goes way beyond glueing little bits of plagiarized training data together
Moreover, LLMs almost ALWAYS extrapolate, and never interpolate. They don't regurgitate training data. Doing so is virtually impossible.
An LLM's input (AND feature) space is enormous. Hundreds or thousands of dimensions. 3D space isn't like 50D or 5,000D space. The space is so combinatorially vast that basically no two points are neighbors. You cannot take your input and "pick something nearby" to a past training example. There IS NO nearby. No convex hull to walk around in. This "curse of dimensionality" wrecks arguments that these models only produce "in distribution" responses. They overwhelmingly can't! (Check out the literature of LeCun et al. for more rigor re. LLM extrapolation.)
LLMs are creative. They work. They push into new areas daily. This reality won't change regardless of how weirdly, desperately the "stochastic parrot" people wish it were otherwise. At this point they're just denialists pushing goalposts around. Don't let 'em get to you!
Moreover, LLMs almost ALWAYS extrapolate, and never interpolate. They don't regurgitate training data. Doing so is virtually impossible.
An LLM's input (AND feature) space is enormous. Hundreds or thousands of dimensions. 3D space isn't like 50D or 5,000D space. The space is so combinatorially vast that basically no two points are neighbors. You cannot take your input and "pick something nearby" to a past training example. There IS NO nearby. No convex hull to walk around in. This "curse of dimensionality" wrecks arguments that these models only produce "in distribution" responses. They overwhelmingly can't! (Check out the literature of LeCun et al. for more rigor re. LLM extrapolation.)
LLMs are creative. They work. They push into new areas daily. This reality won't change regardless of how weirdly, desperately the "stochastic parrot" people wish it were otherwise. At this point they're just denialists pushing goalposts around. Don't let 'em get to you!