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

arXiv: https://arxiv.org/abs/2603.16877 · HTML: https://mlsanity.com/paper/2603.16877/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Legal and financial RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Placed in

- Retrieval-augmented generation / [Legal and financial RAG](https://mlsanity.com/rag/legal-and-financial-rag/)

## Builds on (most cited first, 8 of 8)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [AttentionRAG: Attention-Guided Context Pruning in Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.10720/) (2025)
- [FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.15800/) (2025)
- [AutoNeural: Co-Designing Vision-Language Models for NPU Inference](https://mlsanity.com/paper/2512.02924/) (2025)
- [Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering](https://mlsanity.com/paper/2601.11255/) (2026)

## Built on by (most cited first, 21 of 21)

- [ReTrack: Evidence-Driven Dual-Stream Directional Anchor Calibration Network for Composed Video Retrieval](https://mlsanity.com/paper/2604.17898/) (2026)
- [INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval](https://mlsanity.com/paper/2604.18051/) (2026)
- [HABIT: Chrono-Synergia Robust Progressive Learning Framework for Composed Image Retrieval](https://mlsanity.com/paper/2604.18037/) (2026)
- [Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval](https://mlsanity.com/paper/2604.19386/) (2026)
- [When Safe Skills Collide: Measuring Compositional Risk in Agent Skill Ecosystems](https://mlsanity.com/paper/2606.00448/) (2026)
- [Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict](https://mlsanity.com/paper/2605.14473/) (2026)
- [Relevant Is Not Warranted: Evidence-Force Calibration for Cited RAG](https://mlsanity.com/paper/2605.28044/) (2026)
- [Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-off](https://mlsanity.com/paper/2603.26815/) (2026)
- [Bridging Reasoning Trajectories in On-Policy Distillation via Near-Future Guidance](https://mlsanity.com/paper/2606.00305/) (2026)
- [Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation](https://mlsanity.com/paper/2605.09253/) (2026)
- [GRASP: Plan-Guided Graph Retrieval with Adaptive Fusion and Reranking on Semi-Structured Knowledge Bases](https://mlsanity.com/paper/2605.30237/) (2026)
- [Improving the Completeness and Comparability of Segment Disclosures: A Large Language Model Approach](https://mlsanity.com/paper/2605.23924/) (2026)
- [Detecting High-Potential SMEs with Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2602.19591/) (2026)
- [When LLM Reward Design Fails: Diagnostic-Driven Refinement for Sparse Structured RL](https://mlsanity.com/paper/2605.28918/) (2026)
- [Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite](https://mlsanity.com/paper/2606.11257/) (2026)
- [JailbreakOPT: Tool-Assisted Iterative Jailbreak Prompt Optimization](https://mlsanity.com/paper/2606.11425/) (2026)
- [VisualLeakBench: Reproducible Action-Boundary Propagation Failures in Vision-Language Agents](https://mlsanity.com/paper/2606.07595/) (2026)
- [MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment](https://mlsanity.com/paper/2606.29049/) (2026)
- [RouteCost: A Production-Inspired Multi-Stage Framework for Pre-Order Shipping Cost Estimation in E-Commerce](https://mlsanity.com/paper/2607.16230/) (2026)
- [KumbhDoot: A Scale-Ready, LLM-Bounded Architecture for Mass-Gathering Public-Service Assistants](https://mlsanity.com/paper/2608.07520/) (2026)
- [Better Retrieval, Limited Clustering Gains: A Controlled Study of Multilingual Company Entity Resolution](https://mlsanity.com/paper/2610.05573/) (2026)

## Abstract (arXiv)

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.