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Author: benlebrun

README.md

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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 recent workshop abstract](https://arxiv.org/abs/2306.03081).