AI RESEARCH
Document-Level Numerical Reasoning across Single and Multiple Tables in Financial Reports
arXiv CS.CL
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ArXi:2604.03664v1 Announce Type: new 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.