Text to Query Plans for Question Answering on Large Tables

2025-08-26

AI Summary

To address the challenge non-expert users face in efficiently querying and analyzing large tabular data, this paper proposes an end-to-end framework that translates natural language queries into executable query plans using large language models (LLMs). Unlike conventional SQL generation approaches, our method employs iterative semantic parsing to map natural language into heterogeneous operation sequences—including statistical and machine learning primitives (e.g., PCA, anomaly detection)—and executes them externally to the database, thereby circumventing LLM context-length limitations and the overhead of full-data loading. Experiments on standard benchmarks and large-scale scientific tabular datasets demonstrate substantial improvements in task completion rate and execution efficiency for complex analytical tasks. The framework supports flexible, scalable data analysis beyond the expressive capacity of SQL, offering a novel pathway for the NL2Data analysis paradigm.

Technology Category

• Natural Language Processing: Question Answering
• Data Mining & Knowledge Management: Intelligent Query Processing
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

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 sequences, addressing computational complexity by incrementally building solutions. By executing operations directly on the data, we overcome context length limitations without requiring the entire dataset to be processed by the model. We validate our framework through experiments on both standard databases and large scientific tables, demonstrating its effectiveness in handling extensive datasets and performing sophisticated data analyses.

Problem

• Translating natural language queries into executable query plans
• Overcoming SQL limitations for large dataset analysis
• Enabling complex analytical functions without SQL expertise

Innovation

• Transforms natural language into query plans
• Uses LLMs to iteratively interpret queries
• Executes operations directly on data