NOAH: A System For Analyzing Non-relational Tables With Natural Language

Non-relational tables, such as spreadsheets and web tables, are widely used to store structured data, yet they are primarily designed for human reading rather than machine-driven analysis. These tables often contain hierarchical headers, merged cells, and implicit spatial semantics, making it difficult for large language models (LLMs) to correctly interpret their structure. Moreover, real-world non-relational tables are often large, leading to prohibitive token costs and poor scalability when processed directly by LLMs. To address these challenges, we present NOAH, a system for accurate and scalable analysis of large non-relational tables using natural language queries. Rather than loading the entire table into an LLM or transforming it into a relational format, NOAH constructs a Hierarchical Semantic Index (HSI) in an offline phase that explicitly captures the structure of row and column headers. HSI enables query-driven attribute linking, allowing NOAH to retrieve only the data regions relevant to a given query. Building on HSI, NOAH employs a context-aware data analysis pipeline that progressively resolves complex analytical queries through dynamic operator selection. To further improve efficiency, NOAH incorporates embedding-based hierarchy search, together with a recall-guaranteed dynamic thresholding strategy. We evaluate NOAH on real-world benchmarks for non-relational table analysis. Experimental results demonstrate that NOAH outperforms the best-performing baseline by 31.2% in accuracy, while scaling effectively to large tables and reducing LLM token consumption by at least 3.3×.