DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL

• URL: http://arxiv.org/abs/2503.04959v1
• Date: Thu, 06 Mar 2025 20:46:43 GMT
• Title: DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL
• Authors: Haoyuan Ma, Yongliang Shen, Hengwei Liu, Wenqi Zhang, Haolei Xu, Qiuying Peng, Jun Wang, Weiming Lu,
• Abstract summary: We propose DB-Explore, a novel framework for database understanding using large language models (LLMs)<n>Our framework enables comprehensive database understanding through diverse sampling strategies and automated instruction generation.<n>Our open-source implementation, based on Qwen2.5-coder-7B model, outperforms multiple GPT-4-driven text-to-coder systems in comparative evaluations.
• Score: 18.915121803834698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent text-to-SQL systems powered by large language models (LLMs) have demonstrated remarkable performance in translating natural language queries into SQL. However, these systems often struggle with complex database structures and domain-specific queries, as they primarily focus on enhancing logical reasoning and SQL syntax while overlooking the critical need for comprehensive database understanding. To address this limitation, we propose DB-Explore, a novel framework that systematically aligns LLMs with database knowledge through automated exploration and instruction synthesis. DB-Explore constructs database graphs to capture complex relational schemas, leverages GPT-4 to systematically mine structural patterns and semantic knowledge, and synthesizes instructions to distill this knowledge for efficient fine-tuning of LLMs. Our framework enables comprehensive database understanding through diverse sampling strategies and automated instruction generation, bridging the gap between database structures and language models. Experiments conducted on the SPIDER and BIRD benchmarks validate the effectiveness of DB-Explore, achieving an execution accuracy of 52.1% on BIRD and 84.0% on SPIDER. Notably, our open-source implementation, based on the Qwen2.5-coder-7B model, outperforms multiple GPT-4-driven text-to-SQL systems in comparative evaluations, and achieves near state-of-the-art performance with minimal computational cost.

Related papers

• Bridging the Gap: Transforming Natural Language Questions into SQL Queries via Abstract Query Pattern and Contextual Schema Markup [6.249316460506702]
  We identify two important gaps: the structural mapping gap and the lexical mapping gap.<n> PAS-related achieves an execution accuracy of 87.9%, and leading results on the BIRD dataset with an execution accuracy of 64.67%.<n>Results on the Spider benchmark set a new state-of-the-art on the Spider benchmark with an execution accuracy of 87.9%, and leading results on the BIRD dataset with an execution accuracy of 64.67%.
  arXiv  Detail & Related papers  (2025-02-20T16:11:27Z)
• Bridging the Gap: Enabling Natural Language Queries for NoSQL Databases through Text-to-NoSQL Translation [25.638927795540454]
  We introduce the Text-to-No task, which aims to convert natural language queries into accessible queries.<n>To promote research in this area, we released a large-scale and open-source dataset for this task, named TEND (short interfaces for Text-to-No dataset)<n>We also designed a SLM (Small Language Model)-assisted and RAG (Retrieval-augmented Generation)-assisted multi-step framework called SMART, which is specifically designed for Text-to-No conversion.
  arXiv  Detail & Related papers  (2025-02-16T17:01:48Z)
• RSL-SQL: Robust Schema Linking in Text-to-SQL Generation [51.00761167842468]
  We propose a novel framework called RSL- that combines bidirectional schema linking, contextual information augmentation, binary selection strategy, and multi-turn self-correction. benchmarks demonstrate that our approach achieves SOTA execution accuracy among open-source solutions, with 67.2% on BIRD and 87.9% on GPT-4ocorrection. Our approach outperforms a series of GPT-4 based Text-to-Seek systems when adopting DeepSeek (much cheaper) with same intact prompts.
  arXiv  Detail & Related papers  (2024-10-31T16:22:26Z)
• Enhancing LLM Fine-tuning for Text-to-SQLs by SQL Quality Measurement [1.392448435105643]
  Text-to-s enables non-expert users to effortlessly retrieve desired information from databases using natural language queries. Current state-of-the-art (SOTA) models like GPT4 and T5 have shown impressive performance on large-scale benchmarks like BIRD. This paper proposed a novel approach that only needs SQL Quality to enhance Text-to-s performance.
  arXiv  Detail & Related papers  (2024-10-02T17:21:51Z)
• E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL [1.187832944550453]
  We introduce E-Seek, a novel pipeline specifically designed to address these challenges through direct schema linking and candidate predicate augmentation.<n>E-Seek enhances the natural language query by incorporating relevant database items (i.e., tables, columns, and values) and conditions directly into the question andsql construction plan, bridging the gap between the query and the database structure.<n> Comprehensive evaluations illustrate that E-Seek achieves competitive performance, particularly excelling in complex queries with a 66.29% execution accuracy on the test set.
  arXiv  Detail & Related papers  (2024-09-25T09:02:48Z)
• RB-SQL: A Retrieval-based LLM Framework for Text-to-SQL [48.516004807486745]
  Large language models (LLMs) with in-context learning have significantly improved the performance of text-to- task. We propose RB-, a novel retrieval-based framework for in-context prompt engineering. Experiment results demonstrate that our model achieves better performance than several competitive baselines on public datasets BIRD and Spider.
  arXiv  Detail & Related papers  (2024-07-11T08:19:58Z)
• MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL [47.120862170230566]
  Recent Text-to-yourself methods usually suffer from significant performance degradation on "huge" databases. We introduce MAC, a novel Text-to-yourself LLM-based multi-agent collaborative framework. In our framework, we leverage GPT-4 as the strong backbone for all agent tasks to determine the upper bound of our framework. We then fine-tune an open-sourced instruction-followed model,sql-Llama, by leveraging Code 7B, to accomplish all tasks as GPT-4 does.
  arXiv  Detail & Related papers  (2023-12-18T14:40:20Z)
• DBCopilot: Natural Language Querying over Massive Databases via Schema Routing [47.009638761948466]
  We present DBCopilot, a framework that addresses challenges by employing a compact and flexible copilot model for routing over massive databases.<n>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 joint retrieval manner.
  arXiv  Detail & Related papers  (2023-12-06T12:37:28Z)
• Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation [76.76046657162306]
  Large language models (LLMs) have emerged as a new paradigm for Text-to- task. Large language models (LLMs) have emerged as a new paradigm for Text-to- task.
  arXiv  Detail & Related papers  (2023-08-29T14:59:54Z)
• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) [53.95151604061761]
  This paper introduces the framework for enhancing Text-to- filtering using large language models (LLMs) With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error analyses. With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs.
  arXiv  Detail & Related papers  (2023-05-26T21:39:05Z)
• UNITE: A Unified Benchmark for Text-to-SQL Evaluation [72.72040379293718]
  We introduce a UNIfied benchmark for Text-to-domain systems. It is composed of publicly available text-to-domain datasets and 29K databases. Compared to the widely used Spider benchmark, we introduce a threefold increase in SQL patterns.
  arXiv  Detail & Related papers  (2023-05-25T17:19:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.