February 18, 2024Open Access
Microstructures and Accuracy of Graph Recall by Large Language Models
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Implication
Systematic evaluation reveals biased microstructures and reduced graph recall accuracy in large language models, indicating strong dependence on original domain narration styles.
Key Points
- Large language models frequently underperform during graph recall tasks, showing structural biases toward forming triangles and alternating 2-paths.
- Assessment of microstructures within textually encoded graphs reveals that advanced large language models depend heavily on familiar domain phrasing.
- Highlights the need to mitigate structural distortion during graph reasoning, as biased recall patterns can compromise relational inference in models.
Cite This Study
Wang et al. (2024) studied this question.