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Stochastic Estimation of Transduced Language Models

An unbiased sampler makes prefix probabilities tractable when a transducer maps many source strings to one target prefix. It matters mainly for specialized text or biological sequence systems.

arXiv

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Source Summary

The method samples source prefixes **without replacement** and corrects each with its inverse inclusion probability. Recursive correction yields an **unbiased estimator** and estimates probability mass lost through threshold pruning.

Practical Implication

Builders implementing transduced models can replace an unknown-error lower bound with an estimator whose compute and variance are measurable. The approach is most relevant when transformations admit exponentially many or infinite source strings.

Agent-Ready Context

The method samples source prefixes **without replacement** and corrects each with its inverse inclusion probability. Recursive correction yields an **unbiased estimator** and estimates probability mass lost through threshold pruning.

Builders implementing transduced models can replace an unknown-error lower bound with an estimator whose compute and variance are measurable. The approach is most relevant when transformations admit exponentially many or infinite source strings.

Evidence covers encyclopedic text and DNA. The DNA-to-amino-acid case ran **several orders of magnitude** faster than threshold-pruned beam summing, but the supplied material gives no exact runtime or broader production results.

Context Map

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Uncertainty

Evidence covers encyclopedic text and DNA. The DNA-to-amino-acid case ran **several orders of magnitude** faster than threshold-pruned beam summing, but the supplied material gives no exact runtime or broader production results.