title: Text2SQL is Not Enough: Unifying AI and Databases with TAG
summary: 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.
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paper_authors.name: Asim Biswal
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paper_authors.name: Liana Patel
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paper_authors.name: Siddarth Jha
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paper_authors.fullname: Siddarth Jha
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paper_authors.name: Amog Kamsetty
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paper_authors.fullname: Amog Kamsetty
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paper_authors.name: Shu Liu
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paper_authors.fullname: Shu Liu
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paper_authors.user: lynnliu030
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paper_authors.name: Joseph E. Gonzalez
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paper_authors.fullname: Joseph E. Gonzalez
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paper_authors.user: ProfJoeyG
paper_authors._id: 66ce9234a3ce189b050205d6
paper_authors.name: Carlos Guestrin
paper_authors._id: 66ce9234a3ce189b050205d7
paper_authors.name: Matei Zaharia
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-28T09:48:58.766Z
paper_authors._id: 642509c6d476e4ad5568dd31
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paper_authors.fullname: Matei Zaharia
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paper_authors.user: mateiz
paper_title: Text2SQL is Not Enough: Unifying AI and Databases with TAG
paper_summary: 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.
paper_ai_summary: A unified Table-Augmented Generation (TAG) paradigm addresses limitations of existing Text2SQL and retrieval-based methods, enabling a broader range of natural language queries over databases with improved accuracy and flexibility.
paper_ai_keywords: Text2SQL Retrieval-Augmented Generation RAG Table-Augmented Generation TAG natural language questions relational algebra point lookups data records database interactions world knowledge reasoning capabilities benchmarks