Fine-tuned small language models (3-4B parameters) preserve ordinal consistency in ranking graph structural properties for graphs larger than training data and from held-out families, showing architecture-specific degradation.
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Generalization Boundaries of Fine-Tuned Small Language Models for Graph Structural Inference
Fine-tuned small language models (3-4B parameters) preserve ordinal consistency in ranking graph structural properties for graphs larger than training data and from held-out families, showing architecture-specific degradation.