> We find that meta-in-context learning adaptively modifies priors over latent variables, ultimately leading to priors that closely resemble the true statistics of the environment. Furthermore, our analysis reveals that meta-in-context learning can not only be used to change prior expectations but is also capable of reshaping an LLM’s learning strategies
This was done using OpenAI's public facing APIs
Even if it is not a permanent "edit" it still influences the model at the lowest levels
> We find that meta-in-context learning adaptively modifies priors over latent variables, ultimately leading to priors that closely resemble the true statistics of the environment. Furthermore, our analysis reveals that meta-in-context learning can not only be used to change prior expectations but is also capable of reshaping an LLM’s learning strategies
This was done using OpenAI's public facing APIs
Even if it is not a permanent "edit" it still influences the model at the lowest levels