3 papers
cs.AI2024
Hierarchical Blockmodelling for Knowledge Graphs
Marcin Pietrasik, Marek Reformat, Anna Wilbik
In this paper, we investigate the use of probabilistic graphical models, specifically stochastic blockmodels, for the purpose of hierarchical entity clustering on knowledge graphs.…
cs.CL2023
Negated Complementary Commonsense using Large Language Models
Navid Rezaei, Marek Z. Reformat
Larger language models, such as GPT-3, have shown to be excellent in many tasks. However, we demonstrate that out-of-ordinary questions can throw the model off guard. This work foc…
cs.CL2023
Reinforcement Learning for Topic Models
Jeremy Costello, Marek Z. Reformat
We apply reinforcement learning techniques to topic modeling by replacing the variational autoencoder in ProdLDA with a continuous action space reinforcement learning policy. We tr…