mlsanityData to 7 Oct 2026

Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQL

arXiv 2212.08902 ↗First posted 17 Dec 2022cs.CLcited by 6

Not on the map. Why?

Abstract (arXiv)

The task of text-to-SQL aims to convert a natural language question into its corresponding SQL query within the context of relational tables. Existing text-to-SQL parsers generate a "plausible" SQL query for an arbitrary user question, thereby failing to correctly handle problematic user questions. To formalize this problem, we conduct a preliminary study on the observed ambiguous and unanswerable cases in text-to-SQL and summarize them into 6 feature categories. Correspondingly, we identify the causes behind each category and propose requirements for handling ambiguous and unanswerable questions. Following this study, we propose a simple yet effective counterfactual example generation approach that automatically produces ambiguous and unanswerable text-to-SQL examples. Furthermore, we propose a weakly supervised DTE (Detecting-Then-Explaining) model for error detection, localization, and explanation. Experimental results show that our model achieves the best result on both real-world examples and generated examples compared with various baselines. We release our data and code at: hrefhttps://github.com/wbbeyourself/DTEhttps://github.com/wbbeyourself/DTE.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 17 Dec 2022.

Builds on

16 of 28 references.

References · 16

  1. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  2. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  3. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 509
  4. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  5. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  6. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  7. 2019A General Framework for Uncertainty Estimation in Deep Learningcited by 119
  8. 2020On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queriescited by 59
  9. 2019Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Studycited by 40
  10. 2018Confidence Modeling for Neural Semantic Parsingcited by 33
  11. 2018Design Challenges and Misconceptions in Neural Sequence Labelingcited by 28
  12. 2021Awakening Latent Grounding from Pretrained Language Models for Semantic Parsingcited by 28
4 more
  1. 2020Photon: A Robust Cross-Domain Text-to-SQL Systemcited by 26
  2. 2020"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQLcited by 11
  3. 2018Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categoriescited by 9
  4. 2020ColloQL: Robust Cross-Domain Text-to-SQL Over Search Queriescited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1; 2026: 2.

6 papers.

  1. 2023Benchmarking and Improving Text-to-SQL Generation under Ambiguitycited by 31
  2. 2022Do I have the Knowledge to Answer? Investigating Answerability of Knowledge Base Questionscited by 7
  3. 2023Uncovering and Categorizing Social Biases in Text-to-SQLcited by 5
  4. 2026LatentRefusal: Latent-Signal Refusal for Unanswerable Text-to-SQL Queriescited by 2
  5. 2025Datrics Text2SQL: A Framework for Natural Language to SQL Query Generationcited by 0
  6. 2026TrustDABench: Benchmarking Reliability and Robustness of LLMs for Structured Data Analysiscited by 0