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Small LLMs achieve reliable Text-to-SQL with iterative retry workflows

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new iterative retry workflows to improve the reliability of Text-to-SQL generation using small, resource-constrained language models. By comparing various prompt formulations and specialized models on the Spider benchmark, the study found that SQL-specialized models outperform general-purpose ones at smaller scales. The proposed workflows enhance end-to-end success rates by automatically retrying failed queries with alternative prompts or models, offering a practical method to achieve higher Text-to-SQL performance without resorting to larger, more computationally expensive models. AI

IMPACT Enables more efficient and reliable Text-to-SQL capabilities in applications with limited computational resources.

RANK_REASON The cluster contains an academic paper detailing a new methodology for improving LLM performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Small LLMs achieve reliable Text-to-SQL with iterative retry workflows

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The cluster contains an academic paper detailing a new methodology for improving LLM performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Bilel Abderrahmane Benziane, Xavier Schepler ·

    Reliable Text-to-SQL in Resource-Constrained Settings with Small LLMs via Iterative Retry Workflows

    arXiv:2610.17305v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently improved Text-to-SQL generation, but their computational cost can hinder deployment in resource-constrained settings. This paper evaluates the Text-to-SQL capabilities of small and medium o…

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