7 citations · 23 across the 12 of their papers we have counts for
12 papers
Mitigating Bias in Locally Constrained Decoding via Tractable Proposals
Meihua Dang, Linxin Song, Honghua Zhang +3
Generations from large language models often fail to conform to desired constraints such as JSON schema. Existing locally constrained decoding (LCD) approaches enforce constraints…
Scaling Probabilistic Circuits via Monarch Matrices
Honghua Zhang, Meihua Dang, Benjie Wang +3
Probabilistic Circuits (PCs) are tractable representations of probability distributions allowing for exact and efficient computation of likelihoods and marginals. Recent advancemen…
Restructuring Tractable Probabilistic Circuits
Honghua Zhang, Benjie Wang, Marcelo Arenas +1
Probabilistic circuits (PCs) are a unifying representation for probabilistic models that support tractable inference. Numerous applications of PCs like controllable text generation…
Where is the signal in tokenization space?
Renato Lui Geh, Honghua Zhang, Kareem Ahmed +2
Large Language Models (LLMs) are typically shipped with tokenizers that deterministically encode text into so-called canonical token sequences, to which the LLMs assign probability…
Adaptable Logical Control for Large Language Models
Honghua Zhang, Po-Nien Kung, Masahiro Yoshida +2
Despite the success of Large Language Models (LLMs) on various tasks following human instructions, controlling model generation at inference time poses a persistent challenge. In t…
Polynomial Semantics of Tractable Probabilistic Circuits
Oliver Broadrick, Honghua Zhang, Guy Van den Broeck
Probabilistic circuits compute multilinear polynomials that represent multivariate probability distributions. They are tractable models that support efficient marginal inference. H…
Tractable Control for Autoregressive Language Generation
Honghua Zhang, Meihua Dang, Nanyun Peng +1
Despite the success of autoregressive large language models in text generation, it remains a major challenge to generate text that satisfies complex constraints: sampling from the…
Mixtures of All Trees
Nikil Roashan Selvam, Honghua Zhang, Guy Van den Broeck
Tree-shaped graphical models are widely used for their tractability. However, they unfortunately lack expressive power as they require committing to a particular sparse dependency…
Scaling Up Probabilistic Circuits by Latent Variable Distillation
Anji Liu, Honghua Zhang, Guy Van den Broeck
Probabilistic Circuits (PCs) are a unified framework for tractable probabilistic models that support efficient computation of various probabilistic queries (e.g., marginal probabil…
On the Paradox of Learning to Reason from Data
Honghua Zhang, Liunian Harold Li, Tao Meng +2
Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained end-to-end to solve logical reasoning problems presented in natural language? We attempt to an…
Probabilistic Generating Circuits
Honghua Zhang, Brendan Juba, Guy Van den Broeck
Generating functions, which are widely used in combinatorics and probability theory, encode function values into the coefficients of a polynomial. In this paper, we explore their u…
On the Relationship Between Probabilistic Circuits and Determinantal Point Processes
Honghua Zhang, Steven Holtzen, Guy Van den Broeck
Scaling probabilistic models to large realistic problems and datasets is a key challenge in machine learning. Central to this effort is the development of tractable probabilistic m…