Text2SQL is Not Enough: Unifying AI and Databases with TAG
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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Asim Biswal
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2024-08-28T09:42:57.964Z
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Asim Biswal
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Liana Patel
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2025-09-12T16:27:38.940Z
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LIana
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Siddarth Jha
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2024-08-28T09:48:03.368Z
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Siddarth Jha
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Amog Kamsetty
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2024-08-28T09:48:16.464Z
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Amog Kamsetty
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amogkam
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Shu Liu
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2024-08-29T07:23:35.490Z
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Shu Liu
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lynnliu030
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Joseph E. Gonzalez
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2024-08-28T09:48:41.422Z
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Joseph E. Gonzalez
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ProfJoeyG
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Carlos Guestrin
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Matei Zaharia
admin_assigned
2024-08-28T09:48:58.766Z
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Matei Zaharia
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mateiz
Text2SQL is Not Enough: Unifying AI and Databases with TAG
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.
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.
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