Do Diagrams Help Large Language Models Reason? Evidence from Syllogistic Reasoning
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 ...
| Authors | Risako Ando, Koji Mineshima |
|---|---|
| Primary category | cs.CL |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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