Research: Larger models outperform increased inference compute for text-to-SQL

A new research paper explores the trade-offs between model size and inference compute for grammar-constrained text-to-SQL tasks. The study found that increasing model size generally yields better accuracy than increasing inference compute, even when using techniques like beam search or sample+vote. Specifically, beam search proved more effective than sample+vote at a comparable inference budget, a finding that contrasts with previous research on unconstrained tasks. AI

IMPACT Suggests that for grammar-constrained tasks, prioritizing larger models over complex inference strategies may be more effective.

RANK_REASON The cluster contains an academic paper detailing research findings on LLM inference techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Beam Search
• Grammar-Constrained Text-to-SQL
• Hugging Face
• Qwen2.5-Instruct
• Self-Consistency In Llms
• small language model
• SQL

• paper
• model release

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