Information Extraction From Fiscal Documents Using LLMs

• URL: http://arxiv.org/abs/2511.10659v1
• Date: Mon, 03 Nov 2025 19:17:49 GMT
• Title: Information Extraction From Fiscal Documents Using LLMs
• Authors: Vikram Aggarwal, Jay Kulkarni, Aditi Mascarenhas, Aakriti Narang, Siddarth Raman, Ajay Shah, Susan Thomas,
• Abstract summary: We present a novel approach to extracting structured data from multi-page government fiscal documents.<n>Our method achieves high accuracy through a multi-stage pipeline that leverages domain knowledge, sequential context, and algorithmic validation.<n>Our implementation shows promise for broader applications across developing country contexts.
• Score: 0.44641493866640386
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in text comprehension, but their ability to process complex, hierarchical tabular data remains underexplored. We present a novel approach to extracting structured data from multi-page government fiscal documents using LLM-based techniques. Applied to annual fiscal documents from the State of Karnataka in India (200+ pages), our method achieves high accuracy through a multi-stage pipeline that leverages domain knowledge, sequential context, and algorithmic validation. A large challenge with traditional OCR methods is the inability to verify the accurate extraction of numbers. When applied to fiscal data, the inherent structure of fiscal tables, with totals at each level of the hierarchy, allows for robust internal validation of the extracted data. We use these hierarchical relationships to create multi-level validation checks. We demonstrate that LLMs can read tables and also process document-specific structural hierarchies, offering a scalable process for converting PDF-based fiscal disclosures into research-ready databases. Our implementation shows promise for broader applications across developing country contexts.

Related papers

• LAFA: Agentic LLM-Driven Federated Analytics over Decentralized Data Sources [35.235993431071286]
  Large Language Models (LLMs) have shown great promise in automating data analytics tasks by interpreting natural language queries.<n>Existing LLM-agent-based analytics frameworks operate under the assumption of centralized data access, offering little to no privacy protection.<n>We present LAFA, the first system that integrates LLM-agent-based data analytics with federated analytics.
  arXiv  Detail & Related papers  (2025-10-21T09:56:25Z)
• Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
  Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
  arXiv  Detail & Related papers  (2025-10-17T02:33:16Z)
• Beyond Isolated Dots: Benchmarking Structured Table Construction as Deep Knowledge Extraction [80.88654868264645]
  Arranged and Organized Extraction Benchmark designed to evaluate ability of large language models to comprehend fragmented documents.<n>AOE includes 11 carefully crafted tasks across three diverse domains, requiring models to generate context-specific schema tailored to varied input queries.<n>Results show that even the most advanced models struggled significantly.
  arXiv  Detail & Related papers  (2025-07-22T06:37:51Z)
• Large Language Models are Good Relational Learners [55.40941576497973]
  We introduce Rel-LLM, a novel architecture that utilizes a graph neural network (GNN)- based encoder to generate structured relational prompts for large language models (LLMs)<n>Unlike traditional text-based serialization approaches, our method preserves the inherent relational structure of databases while enabling LLMs to process and reason over complex entity relationships.
  arXiv  Detail & Related papers  (2025-06-06T04:07:55Z)
• Structuring the Unstructured: A Multi-Agent System for Extracting and Querying Financial KPIs and Guidance [54.25184684077833]
  We propose an efficient and scalable method for extracting quantitative insights from unstructured financial documents.<n>Our proposed system consists of two specialized agents: the emphExtraction Agent and the emphText-to-Agent
  arXiv  Detail & Related papers  (2025-05-25T15:45:46Z)
• Unstructured Evidence Attribution for Long Context Query Focused Summarization [53.08341620504465]
  We propose to extract unstructured (i.e., spans of any length) evidence in order to acquire more relevant and consistent evidence than in the fixed granularity case.<n>We show how existing systems struggle to copy and properly cite unstructured evidence, which also tends to be "lost-in-the-middle"
  arXiv  Detail & Related papers  (2025-02-20T09:57:42Z)
• Better Think with Tables: Tabular Structures Enhance LLM Comprehension for Data-Analytics Requests [33.471112091886894]
  Large Language Models (LLMs) often struggle with data-analytics requests related to information retrieval and data manipulation.<n>We introduce Thinking with Tables, where we inject tabular structures into LLMs for data-analytics requests.<n>We show that providing tables yields a 40.29 percent average performance gain along with better manipulation and token efficiency.
  arXiv  Detail & Related papers  (2024-12-22T23:31:03Z)
• TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios [51.66718740300016]
  TableLLM is a robust large language model (LLM) with 8 billion parameters.<n>TableLLM is purpose-built for proficiently handling data manipulation tasks.<n>We have released the model checkpoint, source code, benchmarks, and a web application for user interaction.
  arXiv  Detail & Related papers  (2024-03-28T11:21:12Z)
• Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models [0.0]
  The conventional use of the Retrieval-Augmented Generation architecture has proven effective for retrieving information from diverse documents. This research introduces an innovative approach to enhance the accuracy of complex table queries in RAG-based systems.
  arXiv  Detail & Related papers  (2024-01-04T16:16:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.