mlsanityData to 14 Oct 2026

Grounding Natural Language to SQL Translation with Data-Based Self-Explanations

arXiv 2411.02948 ↗First posted 5 Nov 2024cs.DB cs.CLcited by 8

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Abstract (arXiv)

Natural Language Interfaces for Databases empower non-technical users to interact with data using natural language (NL). Advanced approaches, utilizing either neural sequence-to-sequence or more recent sophisticated large-scale language models, typically implement NL to SQL (NL2SQL) translation in an end-to-end fashion. However, like humans, these end-to-end translation models may not always generate the best SQL output on their first try. In this paper, we propose CycleSQL, an iterative framework designed for end-to-end translation models to autonomously generate the best output through self-evaluation. The main idea of CycleSQL is to introduce data-grounded NL explanations of query results as self-provided feedback, and use the feedback to validate the correctness of the translation iteratively, hence improving the overall translation accuracy. Extensive experiments, including quantitative and qualitative evaluations, are conducted to study CycleSQL by applying it to seven existing translation models on five widely used benchmarks. The results show that 1) the feedback loop introduced in CycleSQL can consistently improve the performance of existing models, and in particular, by applying CycleSQL to RESDSQL, obtains a translation accuracy of 82.0% (+2.6%) on the validation set, and 81.6% (+3.2%) on the test set of Spider benchmark; 2) the generated NL explanations can also provide insightful information for users, aiding in the comprehension of translation results and consequently enhancing the interpretability of NL2SQL translation.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 46 of its 85 references that are in the map. First posted on arXiv 5 Nov 2024.

Builds on

46 of 85 references.

References · 46

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34 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQLcited by 8
  2. 2026An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled Datacited by 2
  3. 2026Every Step Counts: Step-Level Credit Assignment for Tool-Integrated Text-to-SQLcited by 1
  4. 2025From Unstructured Communication to Intelligent RAG: Multi-Agent Automation for Supply Chain Knowledge Basescited by 0
  5. 2025Text2VectorSQL: Towards a Unified Interface for Vector Search and SQL Queriescited by 0
  6. 2026Accurate Table Question Answering with Accessible LLMscited by 0
  7. 2026Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuningcited by 0
  8. 2026Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQLcited by 0