Beam Search, Self-Consistency, and the Limits of Inference-Time Scaling for Grammar-Constrained Text-to-SQL in Small Language Models
Read the original on arXiv Computation and Language →The paper investigates how increasing inference-time computation—via wider beam search or sample‑plus‑vote—affects performance on grammar‑constrained text‑to‑SQL tasks for small language models. Using the Qwen2.5‑Instruct family (0.5B–7B parameters) on the Spider benchmark, the authors find that larger models consistently outperform higher inference compute on the same model size, and that beam search yields better accuracy than sample‑plus‑vote under matched budgets. These results suggest that, unlike unconstrained settings, scaling inference compute does not compensate for smaller model size when strict grammar constraints are applied.
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