I think it’s fine to have tasks that wouldn’t work for today’s language models like those that would require other input modalities. Would prefer to have fully specified inputs but these do seem easy to produce in this case. Would be ideal if there were examples with a smaller input size though.
Hmm. A speculative, currently-intractable way to do this might be to summarize the ML model before feeding it to the goal-extractor.
tl;dr: As per natural abstractions, most of the details of the interactions between the individual neurons are probably irrelevant with regards to the model’s high-level functioning/reasoning. So there should be, in principle, a way to automatically collapse e. g. a trillion-parameters model into a much lower-complexity high-level description that would still preserve such important information as the model’s training objective.
But there aren’t currently any fast-enough algorithms for generating such summaries.
I think it’s fine to have tasks that wouldn’t work for today’s language models like those that would require other input modalities. Would prefer to have fully specified inputs but these do seem easy to produce in this case. Would be ideal if there were examples with a smaller input size though.
Hmm. A speculative, currently-intractable way to do this might be to summarize the ML model before feeding it to the goal-extractor.
tl;dr: As per natural abstractions, most of the details of the interactions between the individual neurons are probably irrelevant with regards to the model’s high-level functioning/reasoning. So there should be, in principle, a way to automatically collapse e. g. a trillion-parameters model into a much lower-complexity high-level description that would still preserve such important information as the model’s training objective.
But there aren’t currently any fast-enough algorithms for generating such summaries.