mlsanityData to 7 Oct 2026

FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis

arXiv 2401.10506 ↗First posted 19 Jan 2024cs.CL cs.AI cs.DBcited by 35

Not on the map. Why?

Abstract (arXiv)

Text-to-SQL, which provides zero-code interface for operating relational databases, has gained much attention in financial analysis; because, financial professionals may not well-skilled in SQL programming. However, until now, there is no practical Text-to-SQL benchmark dataset for financial analysis, and existing Text-to-SQL methods have not considered the unique characteristics of databases in financial applications, such as commonly existing wide tables. To address these issues, we collect a practical Text-to-SQL benchmark dataset and propose a model-agnostic Large Language Model (LLMs)-based Text-to-SQL framework for financial analysis. The benchmark dataset, BULL, is collected from the practical financial analysis business of Hundsun Technologies Inc., including databases for fund, stock, and macro economy. Besides, the proposed LLMs-based Text-to-SQL framework, FinSQL, provides a systematic treatment for financial Text-to-SQL from the perspectives of prompt construction, parameter-efficient fine-tuning and output calibration. Extensive experimental results on BULL demonstrate that FinSQL achieves the state-of-the-art Text-to-SQL performance at a small cost; furthermore, FinSQL can bring up to 36.64% performance improvement in scenarios requiring few-shot cross-database model transfer.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 29 of its 42 references that are in the map. First posted on arXiv 19 Jan 2024.

Builds on

29 of 42 references.

References · 29

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  5. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
  6. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  7. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  8. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
  9. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  10. 2022Teaching Models to Express Their Uncertainty in Wordscited by 731
  11. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  12. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
17 more
  1. 2023Baichuan 2: Open Large-scale Language Modelscited by 664
  2. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
  3. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  4. 2020Revisiting Pre-Trained Models for Chinese Natural Language Processingcited by 262
  5. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 191
  6. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 159
  7. 2023AugGPT: Leveraging ChatGPT for Text Data Augmentationcited by 128
  8. 2020Semantic Evaluation for Text-to-SQL with Distilled Test Suitescited by 123
  9. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  10. 2021KaggleDBQA: Realistic Evaluation of Text-to-SQL Parserscited by 105
  11. 2017Learning a Neural Semantic Parser from User Feedbackcited by 98
  12. 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
  13. 2018SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Taskcited by 74
  14. 2021Natural SQL: Making SQL Easier to Infer from Natural Language Specificationscited by 66
  15. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  16. 2023Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniquescited by 23
  17. 2023Chain of Thought Prompting Elicits Knowledge Augmentationcited by 21

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 8; 2025: 18; 2026: 9.

35 papers.

Sort:

By area:

  1. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 118
  2. 2024A Survey on Employing Large Language Models for Text-to-SQL Taskscited by 67
  3. 2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 54
  4. 2024Large Language Model for Table Processing: A Surveycited by 52
  5. 2024From Natural Language to SQL: Review of LLM-based Text-to-SQL Systemscited by 15
  6. 2025Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errorscited by 14
  7. 2025ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQLcited by 8
  8. 2025AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMscited by 7
  9. 2024DAC: Decomposed Automation Correction for Text-to-SQLcited by 6
  10. 2026EvoSchema: Towards Text-to-SQL Robustness Against Schema Evolutioncited by 5
  11. 2025Bridging Language Models and Financial Analysiscited by 4
  12. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
23 more
  1. 2026DataSpace: Benchmarking Data Agents for Verifiable Analytics over Heterogeneous Workspacescited by 3
  2. 2024Towards Automated Cross-domain Exploratory Data Analysis through Large Language Modelscited by 2
  3. 2025MultiTEND: A Multilingual Benchmark for Natural Language to NoSQL Query Translationcited by 2
  4. 2025RetrySQL: text-to-SQL training with retry data for self-correcting query generationcited by 2
  5. 2025Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligencecited by 2
  6. 2025K-DeCore: Facilitating Knowledge Transfer in Continual Structured Knowledge Reasoning via Knowledge Decouplingcited by 2
  7. 2026Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documentscited by 2
  8. 2025FinStat2SQL: A Text2SQL Pipeline for Financial Statement Analysiscited by 1
  9. 2025CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Descriptioncited by 1
  10. 2025Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoningcited by 1
  11. 2025Revealing the Numeracy Gap: An Empirical Investigation of Text Embedding Modelscited by 1
  12. 2025Beyond SELECT: A Comprehensive Taxonomy-Guided Benchmark for Real-World Text-to-SQL Translationcited by 1
  13. 2025MedInsightBench: Evaluating Medical Analytics Agents Through Multi-Step Insight Discovery in Multimodal Medical Datacited by 1
  14. 2026Both Ends Count! Just How Good are LLM Agents at "Text-to-Big SQL"?cited by 1
  15. 2024A Declarative Query Language for Scientific Machine Learningcited by 0
  16. 2025SPOT: Bridging Natural Language and Geospatial Search for Investigative Journalistscited by 0
  17. 2025GradeSQL: Test-Time Inference with Outcome Reward Models for Text-to-SQL Generation from Large Language Modelscited by 0
  18. 2025FINCH: Financial Intelligence using Natural language for Contextualized SQL Handlingcited by 0
  19. 2026Accurate Table Question Answering with Accessible LLMscited by 0
  20. 2026RingSQL: Schema-Independent Synthetic Data Generation for Text-to-SQL Reinforcement Learningcited by 0
  21. 2026ZAS-SQL: Distilling Rules from Failures for Zero-Shot Text-to-SQLcited by 0
  22. 2026Database Context Compression for Text-to-SQL on Real-World Large Databasescited by 0
  23. 2026Benchmarking Text-to-SQL under Role-Based Access Controlcited by 0