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

arXiv: https://arxiv.org/abs/2401.10506 · HTML: https://mlsanity.com/paper/2401.10506/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 42 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 29 of 29)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Teaching Models to Express Their Uncertainty in Words](https://mlsanity.com/paper/2205.14334/) (2022)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [Baichuan 2: Open Large-scale Language Models](https://mlsanity.com/paper/2309.10305/) (2023)
- [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://mlsanity.com/paper/2304.11015/) (2023)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL](https://mlsanity.com/paper/2302.05965/) (2023)
- [C3: Zero-shot Text-to-SQL with ChatGPT](https://mlsanity.com/paper/2307.07306/) (2023)
- [AugGPT: Leveraging ChatGPT for Text Data Augmentation](https://mlsanity.com/paper/2302.13007/) (2023)
- [Semantic Evaluation for Text-to-SQL with Distilled Test Suites](https://mlsanity.com/paper/2010.02840/) (2020)
- [LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations](https://mlsanity.com/paper/2106.01093/) (2021)
- [KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers](https://mlsanity.com/paper/2106.11455/) (2021)
- [Learning a Neural Semantic Parser from User Feedback](https://mlsanity.com/paper/1704.08760/) (2017)
- [Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing](https://mlsanity.com/paper/2301.07507/) (2023)
- [SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task](https://mlsanity.com/paper/1810.05237/) (2018)
- [Natural SQL: Making SQL Easier to Infer from Natural Language Specifications](https://mlsanity.com/paper/2109.05153/) (2021)
- [Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect](https://mlsanity.com/paper/2208.10099/) (2022)
- [Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques](https://mlsanity.com/paper/2305.17378/) (2023)
- [Chain of Thought Prompting Elicits Knowledge Augmentation](https://mlsanity.com/paper/2307.01640/) (2023)

## Built on by (most cited first, 30 of 36)

- [Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL](https://mlsanity.com/paper/2406.08426/) (2024)
- [A Survey on Employing Large Language Models for Text-to-SQL Tasks](https://mlsanity.com/paper/2407.15186/) (2024)
- [A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?](https://mlsanity.com/paper/2408.05109/) (2024)
- [Large Language Model for Table Processing: A Survey](https://mlsanity.com/paper/2402.05121/) (2024)
- [From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems](https://mlsanity.com/paper/2410.01066/) (2024)
- [Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errors](https://mlsanity.com/paper/2501.09310/) (2025)
- [ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL](https://mlsanity.com/paper/2505.12768/) (2025)
- [AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMs](https://mlsanity.com/paper/2511.05549/) (2025)
- [DAC: Decomposed Automation Correction for Text-to-SQL](https://mlsanity.com/paper/2408.08779/) (2024)
- [Are Your LLM-based Text-to-SQL Models Secure? Exploring SQL Injection via Backdoor Attacks](https://mlsanity.com/paper/2503.05445/) (2025)
- [EvoSchema: Towards Text-to-SQL Robustness Against Schema Evolution](https://mlsanity.com/paper/2603.10697/) (2026)
- [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025)
- [Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities](https://mlsanity.com/paper/2505.23838/) (2025)
- [MultiTEND: A Multilingual Benchmark for Natural Language to NoSQL Query Translation](https://mlsanity.com/paper/2502.11022/) (2025)
- [DataSpace: Benchmarking Data Agents for Verifiable Analytics over Heterogeneous Workspaces](https://mlsanity.com/paper/2608.03451/) (2026)
- [Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models](https://mlsanity.com/paper/2412.07214/) (2024)
- [RetrySQL: text-to-SQL training with retry data for self-correcting query generation](https://mlsanity.com/paper/2507.02529/) (2025)
- [Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence](https://mlsanity.com/paper/2507.10281/) (2025)
- [Revealing the Numeracy Gap: An Empirical Investigation of Text Embedding Models](https://mlsanity.com/paper/2509.05691/) (2025)
- [K-DeCore: Facilitating Knowledge Transfer in Continual Structured Knowledge Reasoning via Knowledge Decoupling](https://mlsanity.com/paper/2509.16929/) (2025)
- [MedInsightBench: Evaluating Medical Analytics Agents Through Multi-Step Insight Discovery in Multimodal Medical Data](https://mlsanity.com/paper/2512.13297/) (2025)
- [Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents](https://mlsanity.com/paper/2608.12342/) (2026)
- [FinStat2SQL: A Text2SQL Pipeline for Financial Statement Analysis](https://mlsanity.com/paper/2506.23273/) (2025)
- [CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description](https://mlsanity.com/paper/2508.12769/) (2025)
- [Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoning](https://mlsanity.com/paper/2508.17905/) (2025)
- [Beyond SELECT: A Comprehensive Taxonomy-Guided Benchmark for Real-World Text-to-SQL Translation](https://mlsanity.com/paper/2511.13590/) (2025)
- [Both Ends Count! Just How Good are LLM Agents at "Text-to-Big SQL"?](https://mlsanity.com/paper/2602.21480/) (2026)
- [A Declarative Query Language for Scientific Machine Learning](https://mlsanity.com/paper/2405.16159/) (2024)
- [SPOT: Bridging Natural Language and Geospatial Search for Investigative Journalists](https://mlsanity.com/paper/2506.13188/) (2025)
- [GradeSQL: Test-Time Inference with Outcome Reward Models for Text-to-SQL Generation from Large Language Models](https://mlsanity.com/paper/2509.01308/) (2025)

## 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.