Metadata-Driven Retrieval-Augmented Generation for Financial Question Answering

• URL: http://arxiv.org/abs/2510.24402v1
• Date: Tue, 28 Oct 2025 13:16:36 GMT
• Title: Metadata-Driven Retrieval-Augmented Generation for Financial Question Answering
• Authors: Michail Dadopoulos, Anestis Ladas, Stratos Moschidis, Ioannis Negkakis,
• Abstract summary: We introduce a sophisticated indexing pipeline to create contextually rich document chunks.<n>We benchmark a spectrum of enhancements, including pre-retrieval filtering, post-retrieval reranking, and enriched embeddings.<n>Our proposed optimal architecture combines LLM-driven pre-retrieval optimizations with these contextual embeddings to achieve superior performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) struggles on long, structured financial filings where relevant evidence is sparse and cross-referenced. This paper presents a systematic investigation of advanced metadata-driven Retrieval-Augmented Generation (RAG) techniques, proposing and evaluating a novel, multi-stage RAG architecture that leverages LLM-generated metadata. We introduce a sophisticated indexing pipeline to create contextually rich document chunks and benchmark a spectrum of enhancements, including pre-retrieval filtering, post-retrieval reranking, and enriched embeddings, benchmarked on the FinanceBench dataset. Our results reveal that while a powerful reranker is essential for precision, the most significant performance gains come from embedding chunk metadata directly with text ("contextual chunks"). Our proposed optimal architecture combines LLM-driven pre-retrieval optimizations with these contextual embeddings to achieve superior performance. Additionally, we present a custom metadata reranker that offers a compelling, cost-effective alternative to commercial solutions, highlighting a practical trade-off between peak performance and operational efficiency. This study provides a blueprint for building robust, metadata-aware RAG systems for financial document analysis.

Related papers

• Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs [66.63911043019294]
  Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them.<n>This paper focuses on the use of LLM techniques to prepare data for diverse downstream tasks.<n>We introduce a task-centric taxonomy that organizes the field into three major tasks: data cleaning, standardization, error processing, imputation, data integration, and data enrichment.
  arXiv  Detail & Related papers  (2026-01-22T12:02:45Z)
• LongDA: Benchmarking LLM Agents for Long-Document Data Analysis [55.32211515932351]
  LongDA targets real-world settings in which navigating long documentation and complex data is the primary bottleneck.<n>LongTA is a tool-augmented agent framework that enables document access, retrieval, and code execution.<n>Our experiments reveal substantial performance gaps even among state-of-the-art models.
  arXiv  Detail & Related papers  (2026-01-05T23:23:16Z)
• A Systematic Framework for Enterprise Knowledge Retrieval: Leveraging LLM-Generated Metadata to Enhance RAG Systems [0.0]
  This research presents a systematic framework for metadata enrichment using large language models (LLMs) to enhance document retrieval in Retrieval-Augmented Generation (RAG) systems.<n>Our approach employs a comprehensive, structured pipeline that dynamically generates meaningful metadata for document segments.
  arXiv  Detail & Related papers  (2025-12-05T04:05:06Z)
• URaG: Unified Retrieval and Generation in Multimodal LLMs for Efficient Long Document Understanding [55.45331924836242]
  We present URaG, a framework that Unifies Retrieval and Generation within a single MLLM.<n>We show that URaG achieves state-of-the-art performance while reducing computational overhead by 44-56%.
  arXiv  Detail & Related papers  (2025-11-13T17:54:09Z)
• Towards Mixed-Modal Retrieval for Universal Retrieval-Augmented Generation [72.34977512403643]
  Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) by retrieving relevant documents from an external corpus.<n>Existing RAG systems primarily focus on unimodal text documents, and often fall short in real-world scenarios where both queries and documents may contain mixed modalities (such as text and images)<n>We propose Nyx, a unified mixed-modal to mixed-modal retriever tailored for Universal Retrieval-Augmented Generation scenarios.
  arXiv  Detail & Related papers  (2025-10-20T09:56:43Z)
• 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)
• FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering [57.18367828883773]
  FinAgentBench is a benchmark for evaluating agentic retrieval with multi-step reasoning in finance.<n>The benchmark consists of 26K expert-annotated examples on S&P-500 listed firms.<n>We evaluate a suite of state-of-the-art models and demonstrate how targeted fine-tuning can significantly improve agentic retrieval performance.
  arXiv  Detail & Related papers  (2025-08-07T22:15:22Z)
• Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models [83.8639566087953]
  We propose a direct retrieval-augmented optimization framework, named DRO, that enables end-to-end training of two key components.<n>DRO alternates between two phases: (i) document permutation estimation and (ii) re-weighted, progressively improving RAG components.<n>Our theoretical analysis reveals that DRO is analogous to policy-gradient methods in reinforcement learning.
  arXiv  Detail & Related papers  (2025-05-05T23:54:53Z)
• Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systems [5.712288463584192]
  Retrieval-Augmented Generation (RAG) has emerged as a promising framework to mitigate hallucinations in Large Language Models (LLMs)<n>In this work, we introduce an efficient, end-to-end RAG pipeline that enhances retrieval for financial documents.
  arXiv  Detail & Related papers  (2025-03-19T13:21:49Z)
• REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark [16.55516587540082]
  We introduce REAL-MM-RAG, an automatically generated benchmark designed to address four key properties essential for real-world retrieval.<n>We propose a multi-difficulty-level scheme based on query rephrasing to evaluate models' semantic understanding beyond keyword matching.<n>Our benchmark reveals significant model weaknesses, particularly in handling table-heavy documents and robustness to query rephrasing.
  arXiv  Detail & Related papers  (2025-02-17T22:10:47Z)
• VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents [66.42579289213941]
  Retrieval-augmented generation (RAG) is an effective technique that enables large language models to utilize external knowledge sources for generation.<n>We introduce VisRAG, which tackles this issue by establishing a vision-language model (VLM)-based RAG pipeline.<n>In this pipeline, instead of first parsing the document to obtain text, the document is directly embedded using a VLM as an image and then retrieved to enhance the generation of a VLM.
  arXiv  Detail & Related papers  (2024-10-14T15:04:18Z)
• Meta Knowledge for Retrieval Augmented Large Language Models [0.0]
  We introduce a novel data-centric RAG workflow for Large Language Models (LLMs) Our methodology relies on generating metadata and synthetic Questions and Answers (QA) for each document. We demonstrate that using augmented queries with synthetic question matching significantly outperforms traditional RAG pipelines.
  arXiv  Detail & Related papers  (2024-08-16T20:55:21Z)
• RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [66.93260816493553]
  This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios.<n>With a focus on factual accuracy, we propose three novel metrics: Completeness, Hallucination, and Irrelevance.<n> Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
  arXiv  Detail & Related papers  (2024-08-02T13:35:11Z)
• Improving Retrieval for RAG based Question Answering Models on Financial Documents [0.046603287532620746]
  This paper explores the existing constraints of RAG pipelines and introduces methodologies for enhancing text retrieval. It delves into strategies such as sophisticated chunking techniques, query expansion, the incorporation of metadata annotations, the application of re-ranking algorithms, and the fine-tuning of embedding algorithms.
  arXiv  Detail & Related papers  (2024-03-23T00:49:40Z)

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.