Table-Augmented Generation (TAG)

Table-Augmented Generation (TAG) is the use of a database query to extract rows of data from a table, which is used as input to a RAG system. This is similar to the return of database chunks from a vector database in a classic RAG system, but offers the advantages of greater personalization and immediacy via up-to-date databases. This architecture is similar to the use of data source plugins for an LLM application, but with a RAG spin on it.

See also more research on related areas:

• Model evaluation
• RAG architectures
• Vector databases
• RAG embedding models
• Advanced RAG architectures
• TAG
• RALM

  Research on TAG

  • Shubham Sharma, September 2, 2024, Table-augmented generation shows promise for complex dataset querying, outperforms text-to-SQL, https://venturebeat.com/data-infrastructure/table-augmented-generation-shows-promise-for-complex-dataset-querying-outperforms-text-to-sql/
  • Sreedevi Gogusetty, Dec 6, 2024, From RAG to TAG: Leveraging the Power of Table-Augmented Generation (TAG): A Leap Beyond Retrieval-Augmented Generation (RAG), https://ai.plainenglish.io/from-rag-to-tag-leveraging-the-power-of-table-augmented-generation-tag-a-leap-beyond-54d1cfadb994 (TAG for augmenting LLMs with queries from database tables, similar to data source plugins.)
  • Tom Martin, Oct 15, 2024, From RAG to TAG: Exploring the Power of Table-Augmented Generation (TAG): A Leap Beyond Retrieval-Augmented Generation (RAG), https://ai.plainenglish.io/from-rag-to-tag-exploring-the-power-of-table-augmented-generation-tag-a-leap-beyond-b2c165309f63
  • Asim Biswal, Liana Patel, Siddarth Jha, Amog Kamsetty, Shu Liu, Joseph E. Gonzalez, Carlos Guestrin, Matei Zaharia, 27 Aug 2024, Text2SQL is Not Enough: Unifying AI and Databases with TAG, https://arxiv.org/abs/2408.14717 Code: https://github.com/TAG-Research/TAG-Bench
  • Zipeng Qiu, You Peng, Guangxin He, Binhang Yuan, Chen Wang, 29 Nov 2024, TQA-Bench: Evaluating LLMs for Multi-Table Question Answering with Scalable Context and Symbolic Extension, https://arxiv.org/abs/2411.19504
  • Kyoungmin Kim, Anastasia Ailamaki, 23 Dec 2024, Trustworthy and Efficient LLMs Meet Databases, https://arxiv.org/abs/2412.18022
  • Mayi Xu, Yunfeng Ning, Yongqi Li, Jianhao Chen, Jintao Wen, Yao Xiao, Shen Zhou, Birong Pan, Zepeng Bao, Xin Miao, Hankun Kang, Ke Sun, Tieyun Qian, 2 Jan 2025, Reasoning based on symbolic and parametric knowledge bases: a survey, https://arxiv.org/abs/2501.01030 (Extensive survey of reasoning from CoT to knowledge graphs to table-based reasoning.)

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