Lachlan McGinness and Peter Baumgartner.
Automated Theorem Provers Help Improve Large Language Model Reasoning.
In Nikolaj Bj{o}rner, Marijn Heule, and Andrei Voronkov, editors, Proceedings of 25th Conference on Logic for Programming, Artificial Intelligence and Reasoning, volume 100 of EPiC Series in Computing, pages 51--69. EasyChair, 2024. [ bib | DOI | http ]

 

Lachlan McGinness and Peter Baumgartner.
Steamroller Problems: An Evaluation of LLM Reasoning Capability with Automated Theorem Prover Strategies, 2024. [ bib | arXiv | http ]

 

Lachlan Mcginness and Peter Baumgartner.
CON-FOLD Explainable Machine Learning with Confidence.
Theory and Practice of Logic Programming, pages 1--19, 2024. [ bib | DOI | Abstract ]

 

Daniel V. Smith, Peter Baumgartner, Lachlan McGinness, Reena Kapoor, Mashud Rana, Ashfaqur Rahman, Andreas Schutt, and Elena Tartaglia.
Activity Recognition within a Manufacturing System: A Comparison of Logic Programming, Machine Learning, and Combinatorial Optimization Based Methods, 2024.
unpublished. [ bib | DOI ]

 

Lachlan McGinness, Peter Baumgartner, Esther Onyango, and Zelalem Lema.
Highlighting Case Studies in LLM Literature Review of Interdisciplinary System Science.
In Mingming Gong, Yiliao Song, Yun Sing Koh, Wei Xiang, and Derui Wang, editors, AI 2024: Advances in Artificial Intelligence, pages 29--43, Singapore, 2024. Springer Nature Singapore. [ bib | DOI | .pdf | Abstract ]