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EviLink improves Text-to-SQL schema linking with uncertainty-guided evidence acquisition

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Researchers have developed EviLink, a novel approach to schema linking for Text-to-SQL systems. This method addresses the challenge of identifying relevant schema context from large databases by reframing schema linking as uncertainty-aware inference over multiple potential SQL paths. EviLink distinguishes between essential schema elements and those that are path-dependent, acquiring evidence only when necessary. Experiments on the BIRD-Dev and Spider2-Snow datasets demonstrate that EviLink improves schema completeness and relevance while managing token costs, achieving a 90.15% field-level strict recall rate on Spider2-Snow. AI

IMPACT Enhances the accuracy and efficiency of Text-to-SQL systems by improving schema linking, potentially leading to better data analysis and query generation.

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 →

EviLink improves Text-to-SQL schema linking with uncertainty-guided evidence acquisition

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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) · Huawei Zheng, Sen Yang, Zhaorui Yang, Yuhui Zhang, Haozhe Feng, Haoxuan Li, Xuan Yi, Chao Hu, Defeng Xie, Chen Hou, Danqing Huang, Wei Chen, Yingcai Wu, Peng Chen, Dazhen Deng ·

    EviLink: Multi-Path Schema Linking with Uncertainty-Guided Evidence Acquisition for Large-Scale Text-to-SQL

    arXiv:2605.29670v1 Announce Type: cross Abstract: Schema linking is a difficult and important step in large-scale Text-to-SQL, where systems must identify a compact yet sufficient schema context from large and ambiguous databases. Existing methods often treat schema linking as de…

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