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LLaMPPL is a research prototype for language model probabilistic programming: specifying language generation tasks by writing probabilistic programs that combine calls to LLMs, symbolic program logic, and probabilistic conditioning. To solve these tasks, LLaMPPL uses a specialized sequential Monte Carlo inference algorithm.

This technique, SMC steering, is described in our workshop abstract, Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs.