I like this idea, and I particularly like that it is amenable to empirical studies. If I were going to tackle this I would use some small synthetic datasets which can be: a) fully known / described. You can decide which portion of the full dataset to be train vs test (vs validation if third split needed)
b) fully learned by toy sized (by today’s standard) models
so, probably like some simple logic puzzles, some limited set of addition or multiplication (e.g. of all three digit numbers), maybe translation between two simple toy languages?
On these I’d want to try some quite different architectures. Some subset of the following which could be made to work well with the dataset chosen (and then try again with a different dataset and different set of architectures):
multilayer perceptrons of different sizes
convolutional neural nets
transformers
LSTMs
multilayer perceptrons with variational auto-encoders acting as information bottlenecks between layers
I think if you could trace the same abstraction across three or four of these types, you’d making get some valuable insights into the generalizable nature of knowledge.
Along the same lines of thought, there are a lot of interpretability techniques which were developed for image models (e.g. CNNs) which I think would be really interesting if generalized to language models / transformers, and seem logically like they would translate pretty easily.
I like this idea, and I particularly like that it is amenable to empirical studies. If I were going to tackle this I would use some small synthetic datasets which can be:
a) fully known / described. You can decide which portion of the full dataset to be train vs test (vs validation if third split needed)
b) fully learned by toy sized (by today’s standard) models
so, probably like some simple logic puzzles, some limited set of addition or multiplication (e.g. of all three digit numbers), maybe translation between two simple toy languages?
On these I’d want to try some quite different architectures. Some subset of the following which could be made to work well with the dataset chosen (and then try again with a different dataset and different set of architectures):
multilayer perceptrons of different sizes
convolutional neural nets
transformers
LSTMs
multilayer perceptrons with variational auto-encoders acting as information bottlenecks between layers
Numenta’s super sparse network idea https://github.com/numenta/htmpapers
maybe a Spiking Neural Net implementation like Nengo https://www.nengo.ai/
xgboost
https://github.com/RichardEvans/apperception an explicit logical model learner
etc...
I think if you could trace the same abstraction across three or four of these types, you’d making get some valuable insights into the generalizable nature of knowledge.
Along the same lines of thought, there are a lot of interpretability techniques which were developed for image models (e.g. CNNs) which I think would be really interesting if generalized to language models / transformers, and seem logically like they would translate pretty easily.