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New SQLMorph framework enhances Text-to-SQL evaluation with query mutation and fine-grained metrics

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SQLMorph, a new framework designed to improve the evaluation of Text-to-SQL systems. This framework employs query mutation techniques, including Join Query Expansion (JQE) and Textual Query Augmentation (TQA), to automatically generate and expand evaluation datasets. SQLMorph also proposes a suite of execution-level metrics, such as Execution Precision (EXP) and Execution Recall (EXR), which offer a more nuanced analysis of system performance compared to traditional binary metrics. AI

IMPACT Improves the reliability and reproducibility of Text-to-SQL system evaluations, crucial for advancing the field.

RANK_REASON The cluster contains a research paper detailing a new framework and methodology for evaluating 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 SQLMorph framework enhances Text-to-SQL evaluation with query mutation and fine-grained metrics

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The cluster contains a research paper detailing a new framework and methodology for evaluating 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) · Mohammadhossein Malekpour, Mohamed Riahi, Maxime Lamothe, Amine Mhedhbi ·

    SQLMorph: Query Mutation and Fine-Grained Metrics for Text-to-SQL Evaluation

    arXiv:2609.08950v1 Announce Type: cross Abstract: Text-to-SQL systems translate natural language queries into executable SQL, democratizing access to structured data. Despite recent advances driven by large language models (LLMs), evaluation remains a major bottleneck: public ben…

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