The paper analyzes language models and identifies a mechanism for binding entities to their attributes. It shows that language models represent binding information through internal activations and that binding vectors form a continuous subspace. This provides insights into how language models represent symbolic knowledge in-context.

https://arxiv.org/abs//2310.17191

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