4 citations · 8 across the 5 of their papers we have counts for
5 papers
Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning
Joonyoung Kim, Kangwook Lee, Haebin Shin +5
The more new features that are being added to smartphones, the harder it becomes for users to find them. This is because the feature names are usually short, and there are just too…
Safe Formulas in the General Theory of Stable Models
Joohyung Lee, Vladimir Lifschitz, Ravi Palla
Safe first-order formulas generalize the concept of a safe rule, which plays an important role in the design of answer set solvers. We show that any safe sentence is equivalent, in…
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…
Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text
Zhun Yang, Adam Ishay, Joohyung Lee
While large language models (LLMs), such as GPT-3, appear to be robust and general, their reasoning ability is not at a level to compete with the best models trained for specific n…