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DBCopilot: Natural Language Querying over Massive Databases via Schema Routing

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arxiv 2312.03463 v3 pith:4XRNFI5D submitted 2023-12-06 cs.CL cs.DBcs.IR

Tianshu Wang , Xiaoyang Chen , Hongyu Lin , Xianpei Han , Le Sun , Hao Wang , Zhenyu Zeng This is my paper
classification cs.CLcs.DBcs.IR
keywords languagedatabasesmassivenaturaldbcopilotgenerationnl2sqlquerying
verification ladder T0 review T1 audit T2 compute T3 formal
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The development of Natural Language Interfaces to Databases (NLIDBs) has been greatly advanced by the advent of large language models (LLMs), which provide an intuitive way to translate natural language (NL) questions into Structured Query Language (SQL) queries. While significant progress has been made in LLM-based NL2SQL, existing approaches face several challenges in real-world scenarios of natural language querying over massive databases. In this paper, we present DBCopilot, a framework that addresses these challenges by employing a compact and flexible copilot model for routing over massive databases. Specifically, DBCopilot decouples schema-agnostic NL2SQL into schema routing and SQL generation. This framework utilizes a single lightweight differentiable search index to construct semantic mappings for massive database schemata, and navigates natural language questions to their target databases and tables in a relation-aware joint retrieval manner. The routed schemata and questions are then fed into LLMs for effective SQL generation. Furthermore, DBCopilot introduces a reverse schema-to-question generation paradigm that can automatically learn and adapt the router over massive databases without manual intervention. Experimental results verify that DBCopilot is a scalable and effective solution for schema-agnostic NL2SQL, providing a significant advance in handling natural language querying over massive databases for NLIDBs.

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Cited by 3 Pith papers

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  1. RASL: Retrieval Augmented Schema Linking for Massive Database Text-to-SQL

    cs.CL 2025-07 conditional novelty 6.0 of 10

    RASL retrieves relevant tables and columns for text-to-SQL by decomposing schemas into semantic entities, calibrating entity-type importance on training data, and using an LLM to rank candidates, beating baselines on ...

  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.

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