mlsanityData to 7 Oct 2026

Text2SQL is Not Enough: Unifying AI and Databases with TAG

arXiv 2408.14717 ↗First posted 27 Aug 2024cs.DB cs.AIcited by 34

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Abstract (arXiv)

AI systems that serve natural language questions over databases promise to unlock tremendous value. Such systems would allow users to leverage the powerful reasoning and knowledge capabilities of language models (LMs) alongside the scalable computational power of data management systems. These combined capabilities would empower users to ask arbitrary natural language questions over custom data sources. However, existing methods and benchmarks insufficiently explore this setting. Text2SQL methods focus solely on natural language questions that can be expressed in relational algebra, representing a small subset of the questions real users wish to ask. Likewise, Retrieval-Augmented Generation (RAG) considers the limited subset of queries that can be answered with point lookups to one or a few data records within the database. We propose Table-Augmented Generation (TAG), a unified and general-purpose paradigm for answering natural language questions over databases. The TAG model represents a wide range of interactions between the LM and database that have been previously unexplored and creates exciting research opportunities for leveraging the world knowledge and reasoning capabilities of LMs over data. We systematically develop benchmarks to study the TAG problem and find that standard methods answer no more than 20% of queries correctly, confirming the need for further research in this area. We release code for the benchmark at https://github.com/TAG-Research/TAG-Bench.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 19 of its 31 references that are in the map. First posted on arXiv 27 Aug 2024.

Builds on

19 of 31 references.

References · 19

  1. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  2. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  3. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  4. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  5. 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 791
  6. 2019Latent Retrieval for Weakly Supervised Open Domain Question Answeringcited by 691
  7. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  8. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
  9. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 286
  10. 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 157
  11. 2020Open-Domain Question Answering Goes Conversational via Question Rewritingcited by 147
  12. 2024InfiAgent-DABench: Evaluating Agents on Data Analysis Taskscited by 134
7 more
  1. 2018TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generationcited by 81
  2. 2021Open Domain Question Answering over Tables via Dense Retrievalcited by 74
  3. 2024Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows?cited by 55
  4. 2024A Declarative System for Optimizing AI Workloadscited by 35
  5. 2024Semantic Operators: A Declarative Model for Rich, AI-based Data Processingcited by 29
  6. 2024Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrievalcited by 27
  7. 2024Optimizing LLM Queries in Relational Data Analytics Workloadscited by 20

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 21; 2026: 10.

34 papers.

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  1. 2024Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wiselycited by 65
  2. 2025VideoRAG: Retrieval-Augmented Generation over Video Corpuscited by 55
  3. 2025AgenticData: An Agentic Data Analytics System for Heterogeneous Datacited by 22
  4. 2024TQA-Bench: Evaluating LLMs for Multi-Table Question Answeringcited by 17
  5. 2025ELT-Bench: An End-to-End Benchmark for Evaluating AI Agents on ELT Pipelinescited by 15
  6. 2025Beyond Quacking: Deep Integration of Language Models and RAG into DuckDBcited by 12
  7. 2025AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQLcited by 10
  8. 2025Weaver: Interweaving SQL and LLM for Table Reasoningcited by 8
  9. 2025Multilingual Text-to-SQL: Benchmarking the Limits of Language Models with Collaborative Language Agentscited by 7
  10. 2025Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputscited by 7
  11. 2026Arming Data Agents with Tribal Knowledgecited by 6
  12. 2024TransitGPT: A Generative AI-based framework for interacting with GTFS data using Large Language Modelscited by 5
22 more
  1. 2025Position: Foundation Models for Tabular Data within Systemic Contexts Need Groundingcited by 5
  2. 2025SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correctioncited by 5
  3. 2025NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answeringcited by 3
  4. 2025Format as a Prior: Quantifying and Analyzing Bias in LLMs for Heterogeneous Datacited by 3
  5. 2025Querying Databases with Function Callingcited by 2
  6. 2025Balancing Content Size in RAG-Text2SQL Systemcited by 2
  7. 2025Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligencecited by 2
  8. 2025A Case for Computing on Unstructured Datacited by 2
  9. 2025LLM-based Question-Answer Framework for Sensor-driven HVAC System Interactioncited by 1
  10. 2025HyST: LLM-Powered Hybrid Retrieval over Semi-Structured Tabular Datacited by 1
  11. 2025DRAMA: Unifying Data Retrieval and Analysis for Open-Domain Analytic Queriescited by 1
  12. 2026GraphSeek: Next-Generation Graph Analytics with LLMscited by 1
  13. 2026Beyond Linear LLM Invocation: An Efficient and Effective Semantic Filter Paradigmcited by 1
  14. 2026Agentic-SQL Revisited: Autonomy-Based Taxonomy and Empirical Benchmark Analysis for LLM Text-to-SQLcited by 1
  15. 2025ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sourcescited by 0
  16. 2025Play by the Type Rules: Inferring Constraints for LLM Functions in Declarative Programscited by 0
  17. 2026PaperGuide: Making Small Language-Model Paper-Reading Agents More Efficientcited by 0
  18. 2026LifeAgentBench: Benchmarking LLMs for Long-Horizon, Cross-Dimensional Lifestyle Health Reasoningcited by 0
  19. 2026SODIUM: From Open Web Data to Queryable Databasescited by 0
  20. 2026Benchmarking Hybrid Deep Research Across Database Querying and Web Searchcited by 0
  21. 2026BI-Agent and BI-Bench: Towards Automating End-to-End Business Intelligencecited by 0
  22. 2026Which Part of the Context Layer Does the Work? Separating Semantic Content from Retrieval Scaffolding in Text-to-SQL Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts
  2. 2025Agentic LLMs for Question Answering over Tabular Data
  3. 2025Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
  4. 2024Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types
  5. 2024Lucy: Think and Reason to Solve Text-to-SQL
  6. 2025AskDB: An LLM Agent for Natural Language Interaction with Relational Databases
  7. 2026Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuning
  8. 2025Agentic NL2SQL to Reduce Computational Costs