4 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
LLM+AL: Bridging Large Language Models and Action Languages for Complex Reasoning about Actions
Adam Ishay, Joohyung Lee
Large Language Models (LLMs) have made significant strides in various intelligent tasks but still struggle with complex action reasoning tasks that require systematic search. To ad…
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…