Stochastic Estimation of Transduced Language Models

Noosaga Placements

• Natural Language Processingsubfield90%
  The paper directly addresses language model estimation, specifically transduced language models that compose pretrained source language models with transducers, and evaluates on text and DNA language modeling tasks.
  Transduced language models (TLMs) compose a pretrained source language model with a functional finite-state transducer to induce a language model over target strings.We evaluate the method on encyclopedic text and DNA against sequential Monte Carlo baselines that resample with replacement.
• Statistical NLPframework90%
  The paper is situated within statistical NLP, as it deals with probabilistic language models, estimation of probabilities, and statistical inference methods like sampling and reweighting.
  Transduced language models (TLMs) compose a pretrained source language model with a functional finite-state transducer to induce a language model over target strings.We show that applying this correction recursively gives an unbiased estimator of the target prefix probability and lets us estimate the mass lost by threshold pruning.
• Probabilistic Aisubfield85%
  The core contribution is a stochastic estimation method with unbiasedness guarantees, using sampling without replacement, Horvitz-Thompson reweighting, and sequential Monte Carlo, which are probabilistic inference techniques.
  Instead, we resample source prefixes without replacement and reweight each selected prefix by the inverse of its inclusion probability.We show that applying this correction recursively gives an unbiased estimator of the target prefix probability and lets us estimate the mass lost by threshold pruning.
• Approximate Inferenceframework85%
  The paper directly uses approximate inference techniques, specifically sampling without replacement and sequential Monte Carlo, to estimate intractable sums over source strings.
  Instead, we resample source prefixes without replacement and reweight each selected prefix by the inverse of its inclusion probability.We evaluate the method on encyclopedic text and DNA against sequential Monte Carlo baselines that resample with replacement.
• Computational Linguisticssubfield70%
  The paper applies the method to a psycholinguistic reading-time analysis, computing word-level surprisal for reading-time prediction, which is a computational linguistics application.
  Replacing threshold pruning with unbiased sampling in a published reading-time analysis substantially lowers the estimated corpus surprisal but leaves the published conclusions unchanged.In psycholinguistics, Kiegeland et al. (2026) use a TLM to derive word-level surprisal from a token-level language model for reading-time prediction.
• Neural NLPframework70%
  The paper deals with neural language models (e.g., GPT-2) as source models in transduced language models, and the method is applied to improve estimation for these models.
  We also run fptb with GPT-2 large, pgpt2 (Radford et al., 2019).On pgpt2 ◦ fptb , beam_swor_adaptive at nominal M =200 takes 772 seconds, compared with 1224 seconds for beam_summing with pruneτ .
• Deep Learningsubfield60%
  The method is applied to pretrained neural language models like GPT-2, and the paper discusses source models that are autoregressive neural networks, though the core contribution is not about deep learning architectures.
  We also run fptb with GPT-2 large, pgpt2 (Radford et al., 2019).On pgpt2 ◦ fptb , beam_swor_adaptive at nominal M =200 takes 772 seconds, compared with 1224 seconds for beam_summing with pruneτ .
• Neural and Deep Learning Approachesframework60%
  The paper applies the method to a computational linguistics task (reading-time prediction) and uses language models, which are central to neural and deep learning approaches in computational linguistics.
  Replacing threshold pruning with unbiased sampling in a published reading-time analysis substantially lowers the estimated corpus surprisal but leaves the published conclusions unchanged.In psycholinguistics, Kiegeland et al. (2026) use a TLM to derive word-level surprisal from a token-level language model for reading-time prediction.
• Machine Learningsubfield50%
  The paper involves estimation theory, variance reduction, and compute-variance tradeoffs, which are machine learning concerns, though the focus is on probabilistic estimation rather than learning algorithms.
  It achieves a better compute–variance tradeoff on text and lower error at the same maximum number of particles on DNA.The resulting estimator is unbiased and replaces unknown pruning error with sampling variance.

Abstract

Transduced language models (TLMs) compose a pretrained \emph{source} language model with a functional finite-state transducer to induce a language model over \emph{target} strings. Computing the probability of a target prefix under a TLM amounts to summing the source-model probabilities of all source strings that the transducer maps to target strings beginning with that prefix. This set can be exponentially large or infinite. Prior work uses a computational shortcut based on source prefix probabilities, then approximates the resulting sum with threshold-pruned beam summing. This produces a lower bound with unknown error. Instead, we resample source prefixes without replacement and reweight each selected prefix by the inverse of its inclusion probability. We show that applying this correction recursively gives an unbiased estimator of the target prefix probability and lets us estimate the mass lost by threshold pruning. Our beam-summing algorithm extends the retained source prefixes and samples which prefixes to keep, reducing their number as more probability mass is added to the running estimate. This can save computation and guarantees that the run halts with probability one. We evaluate the method on encyclopedic text and DNA against sequential Monte Carlo baselines that resample with replacement. It achieves a better compute--variance tradeoff on text and lower error at the same maximum number of particles on DNA. On a DNA-to-amino-acid transduction, it reduces runtime by several orders of magnitude relative to threshold-pruned beam summing and makes estimating prefix probabilities for long target strings feasible. Replacing threshold pruning with unbiased sampling in a published reading-time analysis substantially lowers the estimated corpus surprisal but leaves the published conclusions unchanged.

Paper Context