AI Interpretability idea: Train an LLM with a “split-brain” architecture. That is, the data in the intermediate layers is siloed into two groups with only a small amount of interaction between the groups. This could be enforced in the linear layers by forcing the cross interaction to be a low-rank matrix, or by adding to the regularizer some matrix norm of the cross interaction matrices. I’m sure this can be generalized from MLP to transformer architecture.
Next, apply standard interpretability tools to see how it uses its two sides to represent concepts. Hopefully you’ll find that there are some concepts and tasks that are more focused on one side than on another.
Next, try to elicit the LLM to give verbal descriptions of which concept is represented in which side. Easy mode is to explicitly tell it how it is designed, give some examples of specific concepts primarily encoded on specific sides, even fine-tune train it with those examples, and ask it to predict in which sides are concepts you haven’t trained/told it about before. Hard mode is not to tell it anything about how it was designed but just give it general prompts about trying to introspect try to get it to figure this out from scratch. Maybe hand it off to some of the LLM whisperers without telling it how it was created.
Finally, if you succeed in hard mode, give the same prompting to a regular LLM and pay attention to what it says.
The idea is that I’m trying to elicit the LLM to introspect in a way that actually gives us information about the features and concepts it uses to generate text, rather than just tell the story of the humanoid character it is playacting as. I want to create a scenario where there is a non-obvious ground truth that we have access to that it could plausibly have access to and be able to share.
Yes, that’s just what evolution does, it compresses the enormous amounts of information in the brain into a few gigabytes of data. The decompression algorithm takes a decade or two and involves a lot of looking around and trying things and judging the outcomes of the things that were tried.