LLM Logical Reasoning Related to Aesthetic Universals

The recent surge in popularity of LLMs has led to increased interest in them and to extensive research and evaluation of their reasoning abilities. Induction, deduction, and abduction represent different but interrelated aspects of logical thinking. While each approach serves unique purposes, together they contribute significantly to understanding complex systems and solving complex problems. These reasoning approaches are fundamental in artificial intelligence. Each of them serves different purposes and exhibits specific strengths. In this study, an attempt was made to compare the inductive, deductive and abductive reasoning of three popular LLMs (GPT-4, PaLM-2, LLaMa-2).

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Publication Details

Journal
Proceedings of the Bulgarian Academy of Sciences
Published
2024-12-25
DOI
https://doi.org/10.7546/crabs.2024.12.07
Citations
1
Primary Topic
Rough Sets and Fuzzy Logic
Type
article
Field-Weighted Citation Impact
0.36

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article

LLM Logical Reasoning Related to Aesthetic Universals

Desislava Baeva, Galina Ivanova
1 citations
Proceedings of the Bulgarian Academy of Sciences
Rough Sets and Fuzzy Logic
0.36
article

LLM Logical Reasoning Related to Aesthetic Universals

Desislava Baeva, Galina Ivanova
article en
1 citations

Abstract

The recent surge in popularity of LLMs has led to increased interest in them and to extensive research and evaluation of their reasoning abilities. Induction, deduction, and abduction represent different but interrelated aspects of logical thinking. While each approach serves unique purposes, together they contribute significantly to understanding complex systems and solving complex problems. These reasoning approaches are fundamental in artificial intelligence. Each of them serves different purposes and exhibits specific strengths. In this study, an attempt was made to compare the inductive, deductive and abductive reasoning of three popular LLMs (GPT-4, PaLM-2, LLaMa-2).

Proceedings of the Bulgarian Academy of SciencesVol. 77(12)
Angel Kanchev University of Ruse (BG)
European Commission
Openalex Percentile: Top 33%
Rough Sets and Fuzzy Logic
0.36
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