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Reasoning about concepts with LLMs: Inconsistencies abound

Authors :
Uceda-Sosa, Rosario
Ramamurthy, Karthikeyan Natesan
Chang, Maria
Singh, Moninder
Publication Year :
2024

Abstract

The ability to summarize and organize knowledge into abstract concepts is key to learning and reasoning. Many industrial applications rely on the consistent and systematic use of concepts, especially when dealing with decision-critical knowledge. However, we demonstrate that, when methodically questioned, large language models (LLMs) often display and demonstrate significant inconsistencies in their knowledge. Computationally, the basic aspects of the conceptualization of a given domain can be represented as Is-A hierarchies in a knowledge graph (KG) or ontology, together with a few properties or axioms that enable straightforward reasoning. We show that even simple ontologies can be used to reveal conceptual inconsistencies across several LLMs. We also propose strategies that domain experts can use to evaluate and improve the coverage of key domain concepts in LLMs of various sizes. In particular, we have been able to significantly enhance the performance of LLMs of various sizes with openly available weights using simple knowledge-graph (KG) based prompting strategies.<br />Comment: 15 pages, 5 figures, 3 tables

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2405.20163
Document Type :
Working Paper