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With the recent advances in large language models (LLMs), many commercial table-to-report generators have been released. However, existing systems rarely consider (i) mining potential audit items and (ii) incorporating data-collection notices, both of which are crucial for understanding the table context and the semantics of indices and values. To address this gap, we decouple tabular data understanding into a five-step sequential pipeline, including report framework initialization, table structure parsing, new field discovery, content analysis, and report generation. Empirical experiments and expert assessment show that our prompt-based pipeline can interpret notice files and understand tabular data, thereby generating audit reports. This workflow has been deployed in a data management system to support periodic report generation. Our demo video can be found at: https://youtu.be/9GTAAhoLu8Q .

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Notice-Augmented Real-World Audit Report Generation by Large-Scale Complex Tabular Data Understanding and New Fields Discovery

  • 会议论文

  • 发表日期：

    2026年

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  • Xueyi Zhou,
  • Pei Ye,
  • Dong-Kyu Chae,
  • Zhenyu Li

摘要

With the recent advances in large language models (LLMs), many commercial table-to-report generators have been released. However, existing systems rarely consider (i) mining potential audit items and (ii) incorporating data-collection notices, both of which are crucial for understanding the table context and the semantics of indices and values. To address this gap, we decouple tabular data understanding into a five-step sequential pipeline, including report framework initialization, table structure parsing, new field discovery, content analysis, and report generation. Empirical experiments and expert assessment show that our prompt-based pipeline can interpret notice files and understand tabular data, thereby generating audit reports. This workflow has been deployed in a data management system to support periodic report generation. Our demo video can be found at: https://youtu.be/9GTAAhoLu8Q .