Text2SQL is Not Enough: Unifying AI and Databases with TAG
(2408.14717)Abstract
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.
Overview
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The paper introduces Table-Augmented Generation (TAG) as a new approach to combine the strengths of language models (LMs) and database management systems (DBMSs) to answer complex natural language queries over databases.
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TAG follows a three-step process involving query synthesis, query execution, and answer generation, aiming to overcome the limitations of existing methods such as Text2SQL and Retrieval-Augmented Generation (RAG).
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Empirical evaluation through a comprehensive benchmark demonstrated that TAG significantly outperforms current methods, answering up to 65% of the queries accurately compared to less than 20% by existing techniques.
Unifying AI and Databases with Table-Augmented Generation (TAG)
The paper "Text2SQL is Not Enough: Unifying AI and Databases with TAG" presents Table-Augmented Generation (TAG), a novel paradigm that aims to bridge the capabilities of language models (LMs) and database management systems (DBMSs) to answer natural language queries over databases. This research identifies significant limitations in current methods such as Text2SQL and Retrieval-Augmented Generation (RAG), proposing TAG as a more comprehensive solution.
Introduction and Problem Statement
The authors recognize the transformative potential of enabling users to pose complex natural language questions over data. While existing methods like Text2SQL translate natural language queries into SQL, they are limited to the subset of questions expressible in relational algebra. Similarly, RAG models often fall short when questions demand more than simple data lookups. Text2SQL struggles with queries requiring semantic reasoning or extensive world knowledge, whereas RAG's reliance on point lookup retrievals hampers its efficiency in handling more complex tasks.
The TAG Framework
TAG introduces a three-step process for handling natural language queries:
- Query Synthesis (syn): This step translates the user's natural language request into an executable database query.
- Query Execution (exec): The synthesized query is executed on the database, retrieving relevant data.
- Answer Generation (gen): The LM generates a natural language answer using both the original request and the retrieved data.
These three stages are defined formally as follows:
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Query Synthesis: syn(R) → Q
Query Execution: exec(Q) → T
Answer Generation: gen(R, T) → A
Benchmark and Evaluation
To evaluate the TAG model, the authors developed the first comprehensive benchmark encompassing a wide array of realistic queries. These queries require the integration of LM capabilities with the computational robustness of DBMSs. Evaluating current methods and a hand-written TAG implementation revealed that standard Text2SQL and RAG methods answered less than 20% of the queries correctly. In contrast, the hand-written TAG pipelines achieved accuracy improvements of up to 65%.
Implications and Future Directions
The introduction of TAG has multifaceted implications:
- Practical Applications: TAG's ability to handle complex queries more effectively than existing methods can significantly enhance the way users interact with databases, making data analysis more accessible and intuitive.
- Theoretical Advances: This new paradigm prompts further research into exploring optimal interactions between LMs and DBMSs, particularly in refining the query synthesis and execution processes.
- Future Research: Potential developments include optimized runtime environments for TAG, advancing semantic operator capabilities, and extending TAG's principles to handle multimodal databases.
Conclusion
TAG presents a robust framework that unifies the capabilities of LMs and databases, addressing critical limitations in existing methodologies. Its application shows promise in transforming data interaction and analysis, positioning TAG as a crucial area for ongoing research and development within the AI and database communities.
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GitHub - TAG-Research/TAG-Bench (361 stars)