Large Language Gibbs uses LLM next-token conditionals as MCMC transition operators for iterative resampling of structured variables, aiming to produce a stationary distribution that compromises across all local conditionals.
arXiv preprint arXiv:2307.02390 , year=
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LMT is a Bayesian method that fuses LLM-derived textual priors with temporal Poisson likelihoods to discover causal graphs from alarm event records.
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Structured Inference with Large Language Gibbs
Large Language Gibbs uses LLM next-token conditionals as MCMC transition operators for iterative resampling of structured variables, aiming to produce a stationary distribution that compromises across all local conditionals.
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LMT: A Bayesian Framework for Causal Discovery from Textual Alarm Records in Manufacturing Systems
LMT is a Bayesian method that fuses LLM-derived textual priors with temporal Poisson likelihoods to discover causal graphs from alarm event records.