Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis

• URL: http://arxiv.org/abs/2603.16877v1
• Date: Wed, 18 Feb 2026 20:54:23 GMT
• Title: Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis
• Abstract summary: Retrieval-Augmented Generation system designed to answer questions about S&P 500 financial reports.<n>System employs hybrid search combining full-text and semantic retrieval, followed by an optional reranking stage.<n>Reranking significantly improves answer quality, achieving 49.0 percent correctness for scores of 8 or above.
• Score: 12.26184264751935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Financial analysts face significant challenges extracting information from lengthy 10-K reports, which often exceed 100 pages. This paper presents a Retrieval-Augmented Generation (RAG) system designed to answer questions about S&P 500 financial reports and evaluates the impact of neural reranking on system performance. Our pipeline employs hybrid search combining full-text and semantic retrieval, followed by an optional reranking stage using a cross-encoder model. We conduct systematic evaluation using the FinDER benchmark dataset, comprising 1,500 queries across five experimental groups. Results demonstrate that reranking significantly improves answer quality, achieving 49.0 percent correctness for scores of 8 or above compared to 33.5 percent without reranking, representing a 15.5 percentage point improvement. Additionally, the error rate for completely incorrect answers decreases from 35.3 percent to 22.5 percent. Our findings emphasize the critical role of reranking in financial RAG systems and demonstrate performance improvements over baseline methods through modern language models and refined retrieval strategies.

Related papers

• Enhancing Financial Question Answering: A Novel Benchmark Dataset of Banks' financial statements [6.625637186756314]
  We introduce FinRAG-QA, a novel benchmark dataset for financial question answering.<n>FinRAG-QA targets cross-institutional retrieval over documents averaging 198k words.<n>Contextual chunk enrichment combined with a retrieval-optimised embedding model raises NDCG@10 from 0.322 to 0.710.
  arXiv  Detail & Related papers  (2026-09-03T10:52:56Z)
• SPIEval: Evaluating Large Language Models as Mobile Assistants over Scattered Personal Information [61.41881137371855]
  We introduce SPIEval, a benchmark for large language models (LLMs)<n> SPIEval comprises 250 tasks spanning 4,335 personal records distributed across 10 apps and supports multi-turn interaction through 21 tools.<n>The best-performing model, GPT-5.5 (xhigh), achieves only 57.3% accuracy, while the weakest achieves just 16.4%.
  arXiv  Detail & Related papers  (2026-08-11T09:14:53Z)
• FinRAG-12B: A Production-Validated Recipe for Grounded Question Answering in Banking [0.6356558256426569]
  We present a unified data-efficient framework for training large language models (LLMs)<n>We optimize answer quality, citation grounding, calibrated and refusal under real-world deployment constraints.<n>The system is deployed at 40+ financial institutions, achieving a 7.1 percentage point improvement in query resolution.
  arXiv  Detail & Related papers  (2026-05-06T22:04:44Z)
• From BM25 to Corrective RAG: Benchmarking Retrieval Strategies for Text-and-Table Documents [0.0]
  We benchmark ten retrieval strategies spanning sparse, dense, hybrid fusion, cross-encoder reranking, query expansion, index augmentation, and adaptive retrieval.<n>We evaluate retrieval quality via Recall@k, MRR, and nDCG, and end-to-end generation quality via Number Match.
  arXiv  Detail & Related papers  (2026-04-02T07:53:40Z)
• Test-Time Strategies for More Efficient and Accurate Agentic RAG [58.44913384057518]
  Retrieval-Augmented Generation (RAG) systems face challenges with complex, multihop questions.<n>Such approaches can introduce inefficiencies, including repetitive retrieval of previously processed information.<n>We investigate test-time modifications to the Search-R1 pipeline to mitigate these identified shortcomings.
  arXiv  Detail & Related papers  (2026-03-12T19:18:59Z)
• Rethinking Retrieval: From Traditional Retrieval Augmented Generation to Agentic and Non-Vector Reasoning Systems in the Financial Domain for Large Language Models [0.0]
  We present the first systematic evaluation comparing vector-based agentic RAG using hybrid search and metadata filtering.<n>We measure retrieval metrics (MRR, Recall@5), answer quality through LLM-as-a-judge pairwise comparisons, latency, and preprocessing costs.<n>Our findings reveal that applying advanced RAG techniques to financial Q&A systems improves retrieval accuracy, answer quality, and has cost-performance tradeoffs to be considered in production.
  arXiv  Detail & Related papers  (2025-11-22T20:06:25Z)
• Eigen-1: Adaptive Multi-Agent Refinement with Monitor-Based RAG for Scientific Reasoning [53.45095336430027]
  We develop a unified framework that combines implicit retrieval and structured collaboration.<n>On Humanity's Last Exam (HLE) Bio/Chem Gold, our framework achieves 48.3% accuracy.<n>Results on SuperGPQA and TRQA confirm robustness across domains.
  arXiv  Detail & Related papers  (2025-09-25T14:05:55Z)
• ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization [86.57743637722596]
  Large Language Model (LLM)-based web agents demonstrate strong performance on knowledge-intensive tasks.<n>Complex queries involving multiple entities, intertwined relationships, and high uncertainty demand extensive search cycles.<n>We introduce ReSum, a novel paradigm that enables indefinite exploration through periodic context summarization.
  arXiv  Detail & Related papers  (2025-09-16T17:57:22Z)
• Advanced Multi-Architecture Deep Learning Framework for BIRADS-Based Mammographic Image Retrieval: Comprehensive Performance Analysis with Super-Ensemble Optimization [0.0]
  mammographic image retrieval systems require exact BIRADS categorical matching across five distinct classes.<n>Current medical image retrieval studies suffer from methodological limitations.
  arXiv  Detail & Related papers  (2025-08-06T18:05:18Z)
• Out of Style: RAG's Fragility to Linguistic Variation [29.59506089890902]
  User queries exhibit greater linguistic variations and can trigger cascading errors across interdependent RAG components.<n>We analyze how varying four linguistic dimensions (formality, readability, politeness, and grammatical correctness) impact RAG performance.
  arXiv  Detail & Related papers  (2025-04-11T03:30:26Z)
• 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)
• Unanswerability Evaluation for Retrieval Augmented Generation [74.3022365715597]
  UAEval4RAG is a framework designed to evaluate whether RAG systems can handle unanswerable queries effectively.<n>We define a taxonomy with six unanswerable categories, and UAEval4RAG automatically synthesizes diverse and challenging queries.
  arXiv  Detail & Related papers  (2024-12-16T19:11:55Z)
• Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU [3.1787418271023404]
  We designed a Retrieval-Augmented Generation (RAG) system to provide large language models with relevant documents for answering domain-specific questions. We extracted over 1,800 subpages using a greedy scraping strategy and employed a hybrid annotation process, combining manual and Mistral-generated question-answer pairs. Our RAG framework integrates BM25 and FAISS retrievers, enhanced with a reranker for improved document retrieval accuracy.
  arXiv  Detail & Related papers  (2024-11-20T20:10:43Z)
• Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coverage [74.70255719194819]
  We introduce a novel framework based on sub-question coverage, which measures how well a RAG system addresses different facets of a question. We use this framework to evaluate three commercial generative answer engines: You.com, Perplexity AI, and Bing Chat. We find that while all answer engines cover core sub-questions more often than background or follow-up ones, they still miss around 50% of core sub-questions.
  arXiv  Detail & Related papers  (2024-10-20T22:59:34Z)

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.