|
|
|
|
|
by fn-mote
21 days ago
|
|
My main argument about this kind of work is that it is (essentially if not exactly) in the training set. I want a different argument before I believe that LLMs are doing “out of training dataspace creativity” (extrapolation not interpolation). |
|
Being serious. "Think about what this might mean..." Finding unexpected links between various ideas and background knowledge. What we call a "novel idea" is virtually always the repurposing of an idea/concept in a new context. A system that maps arbitrary inputs into abstraction spaces in which similarities are discoverable, such as say a deep learning system, is perfect for this.