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New Text-to-SQL Method Replaces Training with Memory

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel fine-tuning-free method for Text-to-SQL systems that replaces traditional training with a structured memory approach. This new technique, called MaP-SQL, utilizes reusable memories distilled from training data to encode mappings between natural language, SQL operations, and expected outputs. By aggregating rankings across multiple input permutations and mitigating ordering bias, MaP-SQL improves selection accuracy and efficiency, outperforming previous state-of-the-art methods on benchmarks like BIRD-dev. AI

IMPACT This fine-tuning-free approach could reduce the computational cost of developing advanced Text-to-SQL systems.

RANK_REASON This is a research paper detailing a new method for Text-to-SQL systems. [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 →

New Text-to-SQL Method Replaces Training with Memory

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This is a research paper detailing a new method for Text-to-SQL systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yeonseok Jeong, Soyoung Yoon, Seongjun Lee, Seung-won Hwang ·

    Replacing Training with Memory: Listwise Selection for Text-to-SQL

    arXiv:2609.00834v1 Announce Type: cross Abstract: Modern Text-to-SQL systems often follow generate-execute-select pipelines, generating multiple candidate queries then selecting the best one. Listwise selection, by jointly comparing multiple candidates, has been widely adopted, b…

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