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DTS-SQL: Decomposed Text-to-SQL with Small Large Language Models

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arxiv 2402.01117 v1 pith:L3BSYQDB submitted 2024-02-02 cs.CL cs.DBcs.HC

Mohammadreza Pourreza , Davood Rafiei This is my paper · ORCID
classification cs.CLcs.DBcs.HC
keywords modelslargeproprietarysmallapproachlanguagellmsopen-source
verification ladder T0 review T1 audit T2 compute T3 formal
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Leading models for the text-to-SQL task heavily rely on proprietary Large Language Models (LLMs), posing concerns over data privacy. Closing the performance gap between small open-source models and large proprietary models is crucial to mitigate this reliance. To this end, we introduce a novel two-stage fine-tuning approach that decomposes the task into two simpler tasks. Through comprehensive evaluation on two large cross-domain datasets and two small LLMs, we show that this approach improves execution accuracy by 3 to 7 percent, effectively aligning the performance of open-source models with their proprietary counterparts.

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Cited by 4 Pith papers

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

  1. SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes

    cs.CL 2025-06 conditional novelty 6.0 of 10

    SDE-SQL improves text-to-SQL accuracy by having the model generate and execute exploratory SQL probes to learn database contents before and while writing the final query.

  2. SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases

    cs.CL 2025-05 conditional novelty 5.0 of 10

    By taking the union of all shortest paths between LLM-identified source and destination tables, SchemaGraphSQL reaches 95.71% table recall and 62.91% execution accuracy on BIRD dev, but the SOTA framing depends on rec...

  3. Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities

    cs.CL 2025-05 conditional novelty 4.0 of 10

    A systematic review organizing LLM-based text-to-SQL methods into pre-processing, in-context learning, fine-tuning, and post-processing paradigms, with a catalog of datasets, metrics, challenges, and future directions.

  4. PSM-SQL: Progressive Schema Learning with Multi-granularity Semantics for Text-to-SQL

    cs.DB 2025-02 reject novelty 4.0 of 10

    PSM-SQL iteratively filters tables and columns for text-to-SQL, but its best reported numbers come from a two-module ablation, not from the full three-level multi-granularity method described in the paper.

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