Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models
By Patrik Wolf · Paper · cs.CL
In-context learning is commonly interpreted as a form of conditional inference, in which the prompt specifies a context and the model's output is treated as an estimate of the corresponding conditional distribution. If this interpretation holds, then LLM estimates should satisfy