  Brown et al. (2020) famously introduced the phenomenon of in-context learning
in large language models (LLMs). We establish the existence of a phenomenon we
call meta-out-of-context learning (meta-OCL) via carefully designed synthetic
experiments with LLMs. Our results suggest that meta-OCL leads LLMs to more
readily "internalize" the semantic content of text that is, or appears to be,
broadly useful (such as true statements, or text from authoritative sources)
and use it in appropriate circumstances. We further demonstrate meta-OCL in a
synthetic computer vision setting, and propose two hypotheses for the emergence
of meta-OCL: one relying on the way models store knowledge in their parameters,
and another suggesting that the implicit gradient alignment bias of
gradient-descent-based optimizers may be responsible. Finally, we reflect on
what our results might imply about capabilities of future AI systems, and
discuss potential risks. Our code can be found at
https://github.com/krasheninnikov/internalization.

Machine Learning;Artificial Intelligence