2 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
Automating the Analysis of Parsing Algorithms (and other Dynamic Programs)
Tim Vieira, Ryan Cotterell, Jason Eisner
Much algorithmic research in NLP aims to efficiently manipulate rich formal structures. An algorithm designer typically seeks to provide guarantees about their proposed algorithm -…
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…
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…
Principled Gradient-based Markov Chain Monte Carlo for Text Generation
Li Du, Afra Amini, Lucas Torroba Hennigen +4
Recent papers have demonstrated the possibility of energy-based text generation by adapting gradient-based sampling algorithms, a paradigm of MCMC algorithms that promises fast con…