mlsanityData to 7 Oct 2026

Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback

arXiv 2005.02539 ↗First posted 5 May 2020cs.CLcited by 44

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Abstract (arXiv)

We study the task of semantic parse correction with natural language feedback. Given a natural language utterance, most semantic parsing systems pose the problem as one-shot translation where the utterance is mapped to a corresponding logical form. In this paper, we investigate a more interactive scenario where humans can further interact with the system by providing free-form natural language feedback to correct the system when it generates an inaccurate interpretation of an initial utterance. We focus on natural language to SQL systems and construct, SPLASH, a dataset of utterances, incorrect SQL interpretations and the corresponding natural language feedback. We compare various reference models for the correction task and show that incorporating such a rich form of feedback can significantly improve the overall semantic parsing accuracy while retaining the flexibility of natural language interaction. While we estimated human correction accuracy is 81.5%, our best model achieves only 25.1%, which leaves a large gap for improvement in future research. SPLASH is publicly available at https://aka.ms/Splash_dataset.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 May 2020.

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Citing papers per year: 2021: 11; 2022: 6; 2023: 18; 2024: 5; 2025: 1; 2026: 3.

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  1. 2023Teaching Large Language Models to Self-Debugcited by 963
  2. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  3. 2022Self-critiquing models for assisting human evaluatorscited by 372
  4. 2023MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedbackcited by 293
  5. 2023REFINER: Reasoning Feedback on Intermediate Representationscited by 203
  6. 2023Training Language Models with Language Feedback at Scalecited by 125
  7. 2023Improving Code Generation by Training with Natural Language Feedbackcited by 75
  8. 2023Unleashing the potential of prompt engineering for large language modelscited by 72
  9. 2023Exploring Chain-of-Thought Style Prompting for Text-to-SQLcited by 69
  10. 2022Training Language Models with Language Feedbackcited by 48
  11. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  12. 2021Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Datacited by 43
32 more
  1. 2023EHRSQL: A Practical Text-to-SQL Benchmark for Electronic Health Recordscited by 42
  2. 2021NL-EDIT: Correcting semantic parse errors through natural language interactioncited by 33
  3. 2023Benchmarking and Improving Text-to-SQL Generation under Ambiguitycited by 31
  4. 2023CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Toolscited by 29
  5. 2022Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021cited by 23
  6. 2024BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebracited by 23
  7. 2023Text-to-SQL Error Correction with Language Models of Codecited by 21
  8. 2023Interactive Text-to-SQL Generation via Editable Step-by-Step Explanationscited by 19
  9. 2023Learning to Simulate Natural Language Feedback for Interactive Semantic Parsingcited by 19
  10. 2021Learning Structural Edits via Incremental Tree Transformationscited by 16
  11. 2021NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environmentcited by 15
  12. 2022IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022cited by 14
  13. 2023Transforming Human-Centered AI Collaboration: Redefining Embodied Agents Capabilities through Interactive Grounded Language Instructionscited by 13
  14. 2022Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQLcited by 12
  15. 2021Hierarchical Neural Data Synthesis for Semantic Parsingcited by 11
  16. 2024SQLucid: Grounding Natural Language Database Queries with Interactive Explanationscited by 10
  17. 2021Towards Transparent Interactive Semantic Parsing via Step-by-Step Correctioncited by 9
  18. 2021Interscript: A dataset for interactive learning of scripts through error feedbackcited by 9
  19. 2026SQL-Trail: Multi-Turn Reinforcement Learning with Interleaved Feedback for Text-to-SQLcited by 7
  20. 2021Improving Neural Model Performance through Natural Language Feedback on Their Explanationscited by 5
  21. 2023Correcting Semantic Parses with Natural Language through Dynamic Schema Encodingcited by 2
  22. 2023What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perceptioncited by 2
  23. 2021Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interfacecited by 1
  24. 2021Error-Aware Interactive Semantic Parsing of OpenStreetMapcited by 1
  25. 2024IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agentscited by 1
  26. 2024Understanding the Effect of Algorithm Transparency of Model Explanations in Text-to-SQL Semantic Parsingcited by 1
  27. 2026EGREFINE: An Execution-Grounded Optimization Framework for Text-to-SQL Schema Refinementcited by 1
  28. 2021Metaknowledge Extraction Based on Multi-Modal Documentscited by 0
  29. 2023Semantic Parsing in Limited Resource Conditionscited by 0
  30. 2024The Role of Accuracy and Validation Effectiveness in Conversational Business Analyticscited by 0
  31. 2025VeriMinder: Mitigating Analytical Vulnerabilities in NL2SQLcited by 0
  32. 2026PleaSQLarify: Visual Pragmatic Repair for Natural Language Database Queryingcited by 0