Do Diagrams Help Large Language Models Reason? Evidence from Syllogistic Reasoning
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Computer Science > Computation and Language
Title:Do Diagrams Help Large Language Models Reason? Evidence from Syllogistic Reasoning
Abstract:Diagrams are widely used to support logical reasoning, and prior studies suggest that representations such as Euler diagrams can improve human reasoning performance. Recent work has also explored their effects on large language models (LLMs). In this paper, we compare four representational conditions for syllogistic reasoning: natural language, logical notation, linear diagrams, and Euler diagrams. Using 285 problems from Ando et al. (2024), we evaluate two contemporary LLMs, Claude 3.5~Sonnet and GPT-4o-mini. Our results show that diagrammatic representations do not consistently improve performance. Although the models perform well on entailment and contradiction problems, they struggle with neutral problems and often make systematic conversion errors. Overall, the results suggest that the tested models gain limited benefit from diagrams in logical reasoning tasks.
| Comments: | To appear in the Proceedings of the 15th International Conference on the Theory and Application of Diagrams (Diagrams 2026) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.23513 [cs.CL] |
| (or arXiv:2607.23513v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.23513
arXiv-issued DOI via DataCite (pending registration)
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