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Benjamin LeBrun

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Publications

From Language Models over Tokens to Language Models over Characters
Tim Vieira
Benjamin LeBrun
Mario Giulianelli
Juan Luis Gastaldi
Brian DuSell
John Terilla
Timothy J. O'Donnell
Ryan Cotterell
Modern language models are internally -- and mathematically -- distributions over …
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Language Models over Canonical Byte-Pair Encodings
Tim Vieira
Tianyu Liu
Clemente Pasti
Yahya Emara
Brian DuSell
Benjamin LeBrun
Mario Giulianelli
Juan Luis Gastaldi
Timothy J. O'Donnell
Ryan Cotterell
Modern language models represent probability distributions over character strings as distributions over (shorter) token strings derived via … (see more)a deterministic tokenizer, such as byte-pair encoding. While this approach is highly effective at scaling up language models to large corpora, its current incarnations have a concerning property: the model assigns nonzero probability mass to an exponential number of
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (published)
doi.org
proceedings.mlr.press
Fast Controlled Generation from Language Models with Adaptive Weighted Rejection Sampling
Ben Lipkin
Benjamin LeBrun
Jacob Hoover Vigly
João Loula
David R. MacIver
Lei Du
Jason Eisner
Ryan Cotterell
Vikash Mansinghka
Timothy J. O'Donnell
Alexander K. Lew
Tim Vieira
The dominant approach to generating from language models subject to some constraint is locally constrained decoding (LCD), incrementally sam… (see more)pling tokens at each time step such that the constraint is never violated. Typically, this is achieved through token masking: looping over the vocabulary and excluding non-conforming tokens. There are two important problems with this approach. (i) Evaluating the constraint on every token can be prohibitively expensive---LM vocabularies often exceed 100,000 tokens. (ii) LCD can distort the global distribution over strings, sampling tokens based only on local information, even if they lead down dead-end paths. This work introduces a new algorithm that addresses both these problems. First, to avoid evaluating a constraint on the full vocabulary at each step of generation, we propose an adaptive rejection sampling algorithm that typically requires orders of magnitude fewer constraint evaluations. Second, we show how this algorithm can be extended to produce low-variance, unbiased estimates of importance weights at a very small additional cost---estimates that can be soundly used within previously proposed sequential Monte Carlo algorithms to correct for the myopic behavior of local constraint enforcement. Through extensive empirical evaluation in text-to-SQL, molecular synthesis, goal inference, pattern matching, and JSON domains, we show that our approach is superior to state-of-the-art baselines, supporting a broader class of constraints and improving both runtime and performance. Additional theoretical and empirical analyses show that our method's runtime efficiency is driven by its dynamic use of computation, scaling with the divergence between the unconstrained and constrained LM, and as a consequence, runtime improvements are greater for better models.
2025-07-06
colmweb.org/COLM/2025/Conference (accepted)
openreview.net
openreview.net
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo
João Loula
Benjamin LeBrun
Li Du
Ben Lipkin
Clemente Pasti
Gabriel Grand
Tianyu Liu
Yahya Emara
Marjorie Freedman
Jason Eisner
Ryan Cotterell
Vikash Mansinghka
Alexander K. Lew
Tim Vieira
Timothy J. O'Donnell
A wide range of LM applications require generating text that conforms to syntactic or semantic constraints. Imposing such constraints can be… (see more) naturally framed as probabilistic conditioning, but exact generation from the resulting distribution—which can differ substantially from the LM’s base distribution—is generally intractable. In this work, we develop an architecture for controlled LM generation based on sequential Monte Carlo (SMC). This SMC framework allows us to flexibly incorporate domain- and problem-specific constraints at inferencetime, and efficiently reallocate computational resources in light of new information during the course of generation. By comparing to a number of alternatives and ablations on four challenging domains—Python code generation for data science, text-to-SQL, goal inference, and molecule synthesis—we demonstrate that, with little overhead, our approach allows small open-source language models to outperform models over 8× larger, as well as closed-source, fine-tuned ones. In support of the probabilistic perspective, we show that these performance improvements are driven by better approximation to the posterior distribution. [Our system](https://github.com/probcomp/gen-parse) builds on the framework of Lew et al. (2023) and integrates with its language model probabilistic programming language, giving users a simple, programmable way to apply SMC to a broad variety of controlled generation problems.
2025-01-21
ICLR.cc/2025/Conference (oral)
openreview.net
openreview.net