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cs.CL2024
Refining Answer Distributions for Improved Large Language Model Reasoning
Soumyasundar Pal, Didier Chételat, Yingxue Zhang +1
Large Language Models (LLMs) have exhibited an impressive capability to perform reasoning tasks, especially if they are encouraged to generate a sequence of intermediate steps. Rea…
cs.LG2024
HardCore Generation: Generating Hard UNSAT Problems for Data Augmentation
Joseph Cotnareanu, Zhanguang Zhang, Hui-Ling Zhen +2
Efficiently determining the satisfiability of a boolean equation -- known as the SAT problem for brevity -- is crucial in various industrial problems. Recently, the advent of deep…
cs.LG2024
GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection
Zhanguang Zhang, Didier Chetelat, Joseph Cotnareanu +7
Boolean satisfiability (SAT) problems are routinely solved by SAT solvers in real-life applications, yet solving time can vary drastically between solvers for the same instance. Th…