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Few-Shot Demonstrations Elicit the Use of In-Context World Representations in LLMs
Large language models (LLMs), when acting as agents, are expected to take observed data in context, infer the latent state space underlying the world, and leverage it for downstream prediction. However, prior work demonstrated that LLMs struggle to use representations learned in context on a graph t
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