3 papers
cs.CL2026
LLMs Know More About Numbers than They Can Say
Fengting Yuchi, Li Du, Jason Eisner
Although state-of-the-art LLMs can solve math problems, we find that they make errors on numerical comparisons with mixed notation: "Which is larger, or ?" T…
cs.CL2025
Fast Controlled Generation from Language Models with Adaptive Weighted Rejection Sampling
Benjamin Lipkin, Benjamin LeBrun, Jacob Hoover Vigly +9
The dominant approach to generating from language models subject to some constraint is locally constrained decoding (LCD), incrementally sampling tokens at each time step such that…
cs.CL2025
Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo
João Loula, Benjamin LeBrun, Li Du +12
A wide range of LM applications require generating text that conforms to syntactic or semantic constraints. Imposing such constraints can be naturally framed as probabilistic condi…