Document-Level Numerical Reasoning across Single and Multiple Tables in Financial Reports
Despite the strong language understanding abilities of large language models (LLMs), they still struggle with reliable question answering (QA) over long, structured documents, particularly for numerical reasoning. Financial annual reports exemplify this difficulty: financial statement analysis often hinges on accurate arithmetic, and analysts derive key indicators by integrating evidence scattered across multiple tables and narrative text. However, existing benchmarks focus largely on single-table settings, leaving cross-table document-level numerical reasoning underexplored. To address this gap, we introduce FinLongDocQA, a dataset for both single-table and cross-table financial numerica...
| Authors | Yi-Cheng Wang, Wei-An Wang, Chu-Song Chen |
|---|---|
| Primary category | cs.CL |
| Published | 4 April 2026 |
| Last updated at source | 4 April 2026 |
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