4 citations · 7 across the 7 of their papers we have counts for
5 papers · 1 filter
LLMs as ASP Programmers: Self-Correction Enables Task-Agnostic Nonmonotonic Reasoning
Adam Ishay, Joohyung Lee
Recent large language models (LLMs) have achieved impressive reasoning milestones but continue to struggle with high computational costs, logical inconsistencies, and sharp perform…
Think before You Simulate: Symbolic Reasoning to Orchestrate Neural Computation for Counterfactual Question Answering
Adam Ishay, Zhun Yang, Joohyung Lee +2
Causal and temporal reasoning about video dynamics is a challenging problem. While neuro-symbolic models that combine symbolic reasoning with neural-based perception and prediction…
NeurASP: Embracing Neural Networks into Answer Set Programming
Zhun Yang, Adam Ishay, Joohyung Lee
We present NeurASP, a simple extension of answer set programs by embracing neural networks. By treating the neural network output as the probability distribution over atomic facts…
Leveraging Large Language Models to Generate Answer Set Programs
Adam Ishay, Zhun Yang, Joohyung Lee
Large language models (LLMs), such as GPT-3 and GPT-4, have demonstrated exceptional performance in various natural language processing tasks and have shown the ability to solve ce…
Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer
Zhun Yang, Adam Ishay, Joohyung Lee
Constraint satisfaction problems (CSPs) are about finding values of variables that satisfy the given constraints. We show that Transformer extended with recurrence is a viable appr…