Papers with text-to-SQL
A Review of Cross-Domain Text-to-SQL Models (2020.aacl-srw)
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| Challenge: | WikiSQL and Spider are cross-domain text-to-SQl datasets that have attracted much attention from the research community. |
| Approach: | They propose to divide top models into two paradigms and evaluate their models for schema linking, pretrained word embeddings, reasoning assistance modules. |
| Outcome: | The proposed models have over 90% execution accuracy, the authors show . the proposed models are more complex and more complex than the proposed ones . |
DAC: Decomposed Automation Correction for Text-to-SQL (2025.findings-emnlp)
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| Challenge: | Existing methods to improve text-to-SQL performance are hard to detect errors in SQL directly. |
| Approach: | They propose to use decomposed correction to improve text-to-SQL performance . they first detect errors based on decompose subtasks, then use it to correct them . |
| Outcome: | The proposed method improves text-to-SQL performance by 1.4% compared with previous methods . |
SQL Generation via Machine Reading Comprehension (2020.coling-main)
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| Challenge: | Text-to-SQL systems can generate SQL queries given natural language questions. |
| Approach: | They propose a method that formulates a question answering problem as a query answering problem where different slots are predicted by a unified machine reading comprehension (MRC) model. |
| Outcome: | The proposed method can achieve competitive results on WikiSQL, suggesting it being a promising direction for text-to-SQl. |
PARSQL: Enhancing Text-to-SQL through SQL Parsing and Reasoning (2025.findings-acl)
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| Challenge: | Large language models have made significant strides in text-to-SQL tasks, but small language models struggle to accurately interpret natural language questions due to resource limitations. |
| Approach: | They propose a SQL parser that extracts constraints from SQL to generate sub-SQLs . they use a rule-based and LLM-based method to generate step-by-step SQL explanations based on the results . |
| Outcome: | The proposed framework outperforms models with the same model size on BIRD and Spider benchmarks. |
PExA: Parallel Exploration Agent for Complex Text-to-SQL (2026.acl-short)
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| Challenge: | Recent work in text-to-SQL has explored toolaugmented LLMs, deep planning, and agentic workflows to address complex challenges. |
| Approach: | They validated a framework for text-to-SQL, Spider 2.0, with 70.2% execution accuracy. |
| Outcome: | The proposed framework achieves 70.2% execution accuracy on a state-of-the-art benchmark for text-to-SQL, Spider 2.0. |
Re-appraising the Schema Linking for Text-to-SQL (2023.findings-acl)
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| Challenge: | Recent work has shown that schema linking reduces robustness of text-to-SQL models . EMSL is used to correlate natural language queries with the given database schema . |
| Approach: | They propose a grammar linking module to help model align grammar references with SQL keywords. |
| Outcome: | The proposed language model improves performance without using EMSL, the authors show . their language model is more robust, and the proposed grammar linking improves interoperability . |
Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL (2026.eacl-long)
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| Challenge: | Despite recent advances, performance remains far from clinically reliable . specialized medical terminology and fine-grained temporal reasoning are key to executing clinical data analysis. |
| Approach: | They propose a benchmark for clinical text-to-SQL that demands multi-table joins, clinically meaningful filters, and executable SQL. |
| Outcome: | The proposed benchmark performs well on a set of 20 proprietary and open-source models . it scores 74.7% execution, while DeepSeek-R1 leads open-sourced at 69.2% . |
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL (2024.findings-emnlp)
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| Challenge: | Existing studies have explored selecting relevant demonstrations from a human-labeled demonstration pool, but these methods lack diversity and incur high labeling costs. |
| Approach: | They propose a method that iteratively fuses demonstrations to create a diverse demonstration pool based on human labeling or even from scratch with LLMs, reducing labeling costs. |
| Outcome: | The proposed method achieves an average improvement of 2.1% based on existing labeling and 5.5% from scratch on mainstream datasets. |
SQLSpace: A Representation Space for Text-to-SQL to Discover and Mitigate Robustness Gaps (2025.findings-emnlp)
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| Challenge: | SQLSpace is a representation for text-to-SQL examples derived with minimal human intervention. |
| Approach: | They introduce SQLSpace, a human-interpretable, generalizable, compact representation for text-to-SQL examples . they demonstrate that SQLSpace enables analysis that would be difficult with raw examples alone . |
| Outcome: | The proposed representations are human-interpretable and generalizable . they are used to evaluate models with a granularity beyond overall accuracy scores . |
S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers (2022.findings-acl)
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| Challenge: | Existing graph-based encoders for text-to-SQL do not model the syntax of natural language questions. |
| Approach: | They propose to inject Syntax to question-Schema graph encoder for text-to-SQL parsers and employ the decoupling constraint to induce diverse relational edge embedding. |
| Outcome: | The proposed approach outperforms all existing methods when pre-training models are used, resulting in a performance ranks first on the Spider leaderboard. |
Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing (2021.findings-acl)
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| Challenge: | Recent years pretrained language models (PLMs) have shown their power on modeling language . however, few efforts have been made to explore grounding capabilities of PLMs . |
| Approach: | They propose to use pretrained language models to explore syntactic structures . they propose to combine their approach with an erasingthen-awakening approach . their results show that the approach can awaken latent grounding, which is understandable to humans . |
| Outcome: | Empirical studies show that the proposed approach can awaken latent grounding . it shows great potential to benefit downstream semantic parsing models, it says . |
Structure-Grounded Pretraining for Text-to-SQL (2021.naacl-main)
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Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek, Oleksandr Polozov, Huan Sun, Matthew Richardson
| Challenge: | STRUG is a weakly supervised structure-based pretraining framework for text-to-SQL . it can be used to learn to capture text-table alignment in a given database schema . |
| Approach: | They propose a weakly supervised structure-grounded pretraining framework for text-to-SQL that can effectively learn to capture text-table alignment based on a parallel text-tab corpus. |
| Outcome: | The proposed framework outperforms BERTLARGE and BERTLAGE on all text-to-SQL alignment settings. |
SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising (2022.findings-naacl)
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| Challenge: | Using sketch-based slot filling, text-to-SQL models suffer from over-complexity . et al., e.al., and d.albert, dr., propose a novel method for text- to-Sql generation . |
| Approach: | They propose to train sequence-to-sequence model with Schema-aware Denoising . they propose a clause-sensitive execution guided (EG) decoding strategy . |
| Outcome: | The proposed method improves performance in schema linking and grammar correctness . it also establishes new state-of-the-art on the WikiSQL benchmark . |
Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect (2022.coling-1)
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| Challenge: | text-to-SQL is a language processing and database-based language processing (NLP) task is to convert natural utterances into SQL queries and its practical application is to build natural language interfaces to database systems. |
| Approach: | They propose to conduct a systematic survey of text-to-SQL to examine the challenges and potential future directions. |
| Outcome: | The proposed system converts natural utterances into SQL queries and is a representative task in semantic parsing. |
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL (2022.emnlp-main)
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Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, Zhouhan Lin
| Challenge: | Experimental results show RASAT can leverage a variety of relational structures while inheriting the pretrained parameters from the T5 model. |
| Approach: | They propose a Transformer seq2seq architecture augmented with relation-aware self-attention that leverages relational structures while inheriting pretrained parameters from the T5 model. |
| Outcome: | The proposed model can leverage relational structures while inheriting pretrained parameters from the T5 model effectively. |
TrojanSQL: SQL Injection against Natural Language Interface to Database (2023.emnlp-main)
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| Challenge: | Existing studies on text-to-SQL systems have not investigated its security aspects . however, how to implement such attacks remains an open question. |
| Approach: | They propose a backdoor-based SQL injection framework for text-to-SQL systems that uses boolean-based injection and union-based injecting techniques to exploit SQL injection vulnerabilities. |
| Outcome: | The proposed framework can produce harmful SQL statements invalidating user queries or compromise sensitive information about the database. |
Boosting Text-to-SQL through Multi-grained Error Identification (2025.coling-main)
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| Challenge: | Existing methods for error identification often overlook validation of generated results . text-to-SQL is a technology that converts natural language questions into executable SQL queries . |
| Approach: | They propose to integrate a multi-grained error identification method into existing methods to detect SQL errors. |
| Outcome: | The proposed method can be integrated as a plugin into various methods, providing effective error identification and correction capabilities. |
Large Language Models are good multi-lingual learners : When LLMs meet cross-lingual prompts (2025.coling-main)
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| Challenge: | Experimental results show that Large Language Models can generate rule-based data in long contexts without following all specified rules. |
| Approach: | They propose a novel prompting strategy Multi-Lingual Prompt which automatically translates the error-prone rule that an LLM struggles to follow into another language, thus drawing greater attention to it. |
| Outcome: | The proposed framework outperforms state-of-the-art prompting methods on public datasets across various tasks, with a specific case study in text-to-MIP instances. |
Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) driven by In-Context Learning (ICL) have improved performance of text-to-SQL. |
| Approach: | They propose a strategy to mitigate hallucinations in large language models driven by In-Context Learning (ICL) they propose TA-SQL, a text-to-Sql framework that encourages LLMs to take advantage of similar tasks rather than starting from scratch. |
| Outcome: | The proposed framework improves the performance of the GPT-4 model by 21.23% on BIRD dev. |
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge (2022.emnlp-main)
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Longxu Dou, Yan Gao, Xuqi Liu, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Min-Yen Kan, Jian-Guang Lou
| Challenge: | Existing approaches to text-to-SQL require domain knowledge to parse expert questions into SQL queries. |
| Approach: | They propose a framework to leverage domain knowledge during parsing by building a new benchmark KnowSQL with domain-specific questions. |
| Outcome: | The proposed framework improves the performance of the proposed benchmark by 28.2%. |
Know What I don’t Know: Handling Ambiguous and Unknown Questions for Text-to-SQL (2023.findings-acl)
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| Challenge: | Existing text-to-SQL parsers generate a plausible SQL query for arbitrary user questions, thereby failing to handle problematic user questions. |
| Approach: | They propose a weakly supervised DTE model for error detection, localization, and explanation. |
| Outcome: | The proposed model achieves the best result on real-world examples and generated examples compared with baselines. |
Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL (D19-1)
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| Challenge: | Existing models for text-to-SQL do not explicitly introduce common knowledge to address comparison relations. |
| Approach: | They propose to leverage adjective-noun phrasing knowledge mined from the web to predict comparison relations in text-to-SQL. |
| Outcome: | The proposed approach improves on the original and re-split Spider datasets on comparison relation prediction. |
A Pilot Study for Chinese SQL Semantic Parsing (D19-1)
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| Challenge: | Existing datasets to map natural language text into SQL are limited in their use in question-to-sql mapping. |
| Approach: | They propose to use a Chinese-based semantic parser to map natural language text into SQL. |
| Outcome: | The proposed dataset compares a character-based parser with a word embedding scheme for Chinese . the results show that the parsers are subject to segmentation errors and cross-lingual embedders are useful for text-to-SQL mapping. |
MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL (2025.coling-main)
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| Challenge: | Existing multi-hop retrieval of open-domain text-to-SQL tasks is not applicable due to the tendency to retrieve tables similar to those already retrieved but irrelevant to the question. |
| Approach: | They propose a multi-hop table retrieval with removal task to retrieve unretrieved tables from open-domain text-to-SQL databases. |
| Outcome: | The proposed method improves performance 5.7% over the previous state-of-the-art methods on open-domain text-to-SQL datasets. |
Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL (2024.findings-emnlp)
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| Challenge: | Existing text-to-SQL LLMs are computationally expensive and difficult to deploy in real-world applications. |
| Approach: | They propose to distill a larger teacher model into a smaller student model by using imperfect data to improve the KD. |
| Outcome: | The proposed method achieves the best tradeoff between performance and efficiency on 5 text-to-SQL benchmarks. |
“What Do You Mean by That?” A Parser-Independent Interactive Approach for Enhancing Text-to-SQL (2020.emnlp-main)
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| Challenge: | In Natural Language Interfaces to Databases systems, text-to-SQL parsers allow users to query databases by using natural language questions. |
| Approach: | They propose a parser-independent interactive approach that interacts with users using multi-choice questions and can easily work with arbitrary parsers. |
| Outcome: | The proposed approach improves performance with limited interaction turns by using simulation and human evaluation on two cross-domain datasets with five state-of-the-art parsers. |
Retrieve Only Relevant Tables Whether Few or Many: Adaptive Table Retrieval Method (2026.findings-acl)
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| Challenge: | Existing methods for retrieving relevant tables from databases are limited by the number of tables required. |
| Approach: | They propose an adaptive table retrieval method that adjusts the number of tables retrieved according to the requirements of each query. |
| Outcome: | Experiments on Spider, BIRD, and Spider 2.0 show that the proposed method improves performance and retrieval and downstream tasks. |
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm (2024.findings-acl)
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Yuanzhen Xie, Xinzhou Jin, Tao Xie, Matrixmxlin Matrixmxlin, Liang Chen, Chenyun Yu, Cheng Lei, Chengxiang Zhuo, Bo Hu, Zang Li
| Challenge: | In-context learning of large-language models has achieved remarkable success in the field of natural language processing . however, the single-step chain-of-thought prompting approach faces challenges such as attention diffusion and inadequate performance in complex tasks like text-to-SQL. |
| Approach: | They propose a workflow paradigm method to enhance the attention and problem-solving scope of large-language models through decomposition. |
| Outcome: | The proposed method outperforms existing methods on three datasets and improves the upper limit of LLM-based approaches. |
Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM (2024.findings-acl)
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| Challenge: | Existing methods for Generating accurate SQL queries for user questions rely on the capability of large language models (LLMs) however, some knowledge is not explicitly included in the database schema and user question or has been learned by LLMs. |
| Approach: | They propose a Knowledge-to-SQL framework that employs tailored Data Expert LLM (DELLM) to provide helpful knowledge for all text-to SQL models. |
| Outcome: | The proposed framework improves the state-of-the-art approaches for text-to-SQL tasks by leveraging a data expert LLM (DELLM) to provide useful knowledge for all text- to-SqL models. |
SIMBA UQ: Similarity-Based Aggregation for Uncertainty Quantification in Large Language Models (2025.findings-emnlp)
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Debarun Bhattacharjya, Balaji Ganesan, Junkyu Lee, Radu Marinescu, Katya Mirylenka, Michael Glass, Xiao Shou
| Challenge: | Uncertainty quantification (UQ) provides measures of uncertainty, such as an estimate of the confidence in an LLM’s generated output. |
| Approach: | They propose a black-box approach where consistency is used as a proxy for confidence in a model's output. |
| Outcome: | The proposed methods are primarily but not necessarily entirely black- box, with consistency between output and other sampled generations used as a proxy for confidence in its correctness. |
Optimizing Reasoning for Text-to-SQL with Execution Feedback (2025.findings-acl)
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| Challenge: | Large language models excel in many reasoning tasks, but their ability to leverage Chain-of-Thought (CoT) reasoning remains underexplored. |
| Approach: | They propose a framework that iteratively optimizes open-source LLMs by combining CoT reasoning with off-policy and on-poly DPO, relying solely on execution accuracy as feedback. |
| Outcome: | The proposed framework improves execution accuracy on BIRD and Spider datasets. |
STaR-SQL: Self-Taught Reasoner for Text-to-SQL (2025.acl-long)
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| Challenge: | Existing methods for generating step-by-step “chain-of-thought” rationales are limited to text-to-SQL. |
| Approach: | They propose a method that prompts SQL query generation to produce reasoning steps for SQL queries and fine-tunes it on rationales that lead to correct outcomes. |
| Outcome: | The proposed method outperforms agent-like prompting methods on the Spider benchmark. |
ExeSQL: Self-Taught Text-to-SQL Models with Execution-Driven Bootstrapping for SQL Dialects (2025.findings-emnlp)
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| Challenge: | Existing text-to-SQL models are limited to SQLite due to dataset limitations . data generated through static prompting is noisy and unreliable, authors say . |
| Approach: | They propose a text-to-SQL framework with execution-driven, agentic bootstrapping . ExeSQl bridges the dialect gap in text- to-Sql, achieving average improvements . |
| Outcome: | ExeSQL bridges the dialect gap in text-to-SQl, with average improvements of 15.2%, 10.38%, and 4.49% over GPT-4o on PostgreSQLE, MySQL, and Oracle. |
Knowledge Base Construction for Knowledge-Augmented Text-to-SQL (2025.findings-acl)
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Jinheon Baek, Horst Samulowitz, Oktie Hassanzadeh, Dharmashankar Subramanian, Sola Shirai, Alfio Gliozzo, Debarun Bhattacharjya
| Challenge: | Existing approaches to translate natural language queries into SQL statements are limited in their parametric knowledge of the database schemas. |
| Approach: | They propose to construct a knowledge base for text-to-SQL, a foundational source of knowledge, from which we retrieve and generate the necessary knowledge for given queries. |
| Outcome: | The proposed approach outperforms baselines on multiple text-to-SQL datasets and shows that it is practical and reliable. |
GenLink: Generation-Driven Schema-Linking via Multi-Model Learning for Text-to-SQL (2025.emnlp-main)
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| Challenge: | Experimental results on BIRD and Spider benchmarks validate the effectiveness of GenLink. |
| Approach: | They propose a generation-driven schema-linking framework based on multi-model learning . experimental results validate the effectiveness of GenLink . |
| Outcome: | Experimental results show that GenLink improves schema-linking recall rate and cross-domain adaptability. |
VET: Verifiable Execution Tracing for Reliable Text-to-SQL Generation (2026.findings-acl)
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| Challenge: | Existing methods for text-to-SQL generation are prone to hallucinations and grounding . authors present a novel reasoning paradigm that transforms text- to-Sql from unverifiable textual rationales into step-wise executable semantics. |
| Approach: | They propose a reasoning paradigm that transforms text-to-SQL from unverifiable textual rationales into step-wise executable semantics. |
| Outcome: | The proposed reasoning paradigm transforms text-to-SQL from unverifiable textual rationales into step-wise executable semantics. |