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To correctly use in-context information, language models (LMs) must bindentities to their attributes. For example, given a context describing a "greensquare" and a "blue circle", LMs must bind the shapes to their respectivecolors. We analyze LM representations and identify the binding ID mechanism: ageneral mechanism for solving the binding problem, which we observe in everysufficiently large model from the Pythia and LLaMA families. Using causalinterventions, we show that LMs' internal activations represent bindinginformation by attaching binding ID vectors to corresponding entities andattributes. We further show that binding ID vectors form a continuous subspace,in which distances between binding ID vectors reflect their discernability.Overall, our results uncover interpretable strategies in LMs for representingsymbolic knowledge in-context, providing a step towards understanding generalin-context reasoning in large-scale LMs.

How do Language Models Bind Entities in Context?