Text to Query Plans for Question Answering on Large Tables
Yipeng Zhang, Chen Wang, Yuzhe Zhang, Jacky Jiang

TL;DR
This paper introduces a framework that converts natural language questions into executable query plans for large tables, supporting complex analyses and overcoming SQL limitations by leveraging LLMs and direct data execution.
Contribution
The authors present a novel outside-database system that transforms natural language into query plans, enabling complex data analysis without SQL's inefficiencies.
Findings
Effective handling of large datasets demonstrated in experiments.
Supports complex analytical functions like PCA and anomaly detection.
Outperforms traditional SQL-based approaches in flexibility and scalability.
Abstract
Efficient querying and analysis of large tabular datasets remain significant challenges, especially for users without expertise in programming languages like SQL. Text-to-SQL approaches have shown promising performance on benchmark data; however, they inherit SQL's drawbacks, including inefficiency with large datasets and limited support for complex data analyses beyond basic querying. We propose a novel framework that transforms natural language queries into query plans. Our solution is implemented outside traditional databases, allowing us to support classical SQL commands while avoiding SQL's inherent limitations. Additionally, we enable complex analytical functions, such as principal component analysis and anomaly detection, providing greater flexibility and extensibility than traditional SQL capabilities. We leverage LLMs to iteratively interpret queries and construct operation…
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