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RSL-SQL: Robust Schema Linking in Text-to-SQL Generation

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arxiv 2411.00073 v2 pith:ZQPF4WIG submitted 2024-10-31 cs.CL cs.AIcs.DB

Zhenbiao Cao , Yuanlei Zheng , Zhihao Fan , Xiaojin Zhang , Wei Chen , Xiang Bai This is my paper
classification cs.CLcs.AIcs.DB
keywords linkingschematext-to-sqlrsl-sqlapproachbirdcontextualelements
verification ladder T0 review T1 audit T2 compute T3 formal
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Text-to-SQL generation aims to translate natural language questions into SQL statements. In Text-to-SQL based on large language models, schema linking is a widely adopted strategy to streamline the input for LLMs by selecting only relevant schema elements, therefore reducing noise and computational overhead. However, schema linking faces risks that require caution, including the potential omission of necessary elements and disruption of database structural integrity. To address these challenges, we propose a novel framework called RSL-SQL that combines bidirectional schema linking, contextual information augmentation, binary selection strategy, and multi-turn self-correction. We improve the recall of pattern linking using forward and backward pruning methods, achieving a strict recall of 94% while reducing the number of input columns by 83%. Furthermore, it hedges the risk by voting between a full mode and a simplified mode enhanced with contextual information. Experiments on the BIRD and Spider benchmarks demonstrate that our approach achieves SOTA execution accuracy among open-source solutions, with 67.2% on BIRD and 87.9% on Spider using GPT-4o. Furthermore, our approach outperforms a series of GPT-4 based Text-to-SQL systems when adopting DeepSeek (much cheaper) with same intact prompts. Extensive analysis and ablation studies confirm the effectiveness of each component in our framework. The codes are available at https://github.com/Laqcce-cao/RSL-SQL.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. EcoTable: Cost-effective Table Integration in Data Lakes for Natural Language Queries

    cs.DB 2026-06 unverdicted novelty 6.0 of 10

    Query-driven table integration that uses Steiner-tree search to choose which joins LLMs must verify, reporting 30%+ accuracy gains at 5x lower LLM cost.

  2. ErrorLLM: Modeling SQL Errors for Text-to-SQL Refinement

    cs.CL 2026-03 conditional novelty 6.0 of 10

    A fine-tuned model with special error tokens detects SQL mistakes and guides LLMs to fix them, raising execution accuracy on BIRD from 55.87% to 66.23% and on Spider from 75.44% to 86.94% over GPT-4o.

  3. SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation

    cs.CL 2025-06 conditional novelty 6.0 of 10

    SEED automatically generates evidence from database schemas, descriptions, and sampled values, improving text-to-SQL accuracy in no-evidence settings.

  4. RAISE: Reasoning Agent for Interactive SQL Exploration

    cs.AI 2025-06 conditional novelty 6.0 of 10

    An interactive reasoning agent that explores databases with live queries raises text-to-SQL execution accuracy from 44.8% to 56.5% and achieves 81.8% best-of-N coverage on BIRD.

  5. AttnLink: Turning Attention into Schema Links for Text-to-SQL

    cs.CL 2026-08 conditional novelty 5.0 of 10

    Attention from an LLM's generation-start position, pooled over schema candidates, yields millisecond-scale, controllable schema-linking scores that beat prior linkers on three benchmarks.

  6. APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL

    cs.DB 2026-02 conditional novelty 5.0 of 10

    An agentic text-to-SQL pipeline using hypothesis-verification exploration achieves 70.7% execution accuracy on BIRD-Dev and 51.0% on Spider 2.0-Snow.

  7. Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages

    cs.CL 2025-06 conditional novelty 5.0 of 10

    Generating Python programs as intermediate guidance before SQL, then voting on Python execution results to select the fastest matching SQL, improves text-to-SQL execution accuracy and efficiency on BIRD and Archer.

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