I think this and somewhat related approaches (eg, the Alpaca approach of generating RLHF data from chatGPT and training a smaller model with it) are really inte

I think this and somewhat related approaches (eg, the Alpaca approach of generating RLHF data from chatGPT and training a smaller model with it) are really interesting. If we think of intelligence as the ability to reverse entropy (as in being able to compress information into the smallest representation) there's this weird conceptual idea where a strong enough model is able to transfer its knowledge like this without wandering off track and teaching the wrong things. It'd be interesting to have formal benchmarks of this ability because I think it is closely related to things people care about in the generative space. For example H2O recently introduced a nice new LLM with a good license. But informally in my testing is seems to hallucinate much worse than Llama derived models - for example it my testing it hallucinated that "Crown Prince Harry" (!) married Phillip Mountbatten (!!)[1]. I suspect but can't prove that a model like this would be much worse at distilling than a Llama based model of the same size. Testing this knowledge transfer would be really instructive I think. [1] https://twitter.com/nlothian/status/1653267700288987138 https://twitter.com/nlothian/status/1653267700288987138