# Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systems

arXiv: https://arxiv.org/abs/2503.15191 · HTML: https://mlsanity.com/paper/2503.15191/

## Key facts

- Cited by 9 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 30 of its 40 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Placed in

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

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [MTEB: Massive Text Embedding Benchmark](https://mlsanity.com/paper/2210.07316/) (2022)
- [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://mlsanity.com/paper/2109.00122/) (2021)
- [TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://mlsanity.com/paper/2105.07624/) (2021)
- [LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models](https://mlsanity.com/paper/2310.05736/) (2023)
- [FinanceBench: A New Benchmark for Financial Question Answering](https://mlsanity.com/paper/2311.11944/) (2023)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering](https://mlsanity.com/paper/2210.03849/) (2022)
- [Learning a Deep Embedding Model for Zero-Shot Learning](https://mlsanity.com/paper/1611.05088/) (2016)
- [How do Large Language Models Handle Multilingualism?](https://mlsanity.com/paper/2402.18815/) (2024)
- [Query2doc: Query Expansion with Large Language Models](https://mlsanity.com/paper/2303.07678/) (2023)
- [MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data](https://mlsanity.com/paper/2206.01347/) (2022)
- [A Theoretical Analysis of NDCG Type Ranking Measures](https://mlsanity.com/paper/1304.6480/) (2013)
- [Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers](https://mlsanity.com/paper/2404.07220/) (2024)
- [Improving Retrieval for RAG based Question Answering Models on Financial Documents](https://mlsanity.com/paper/2404.07221/) (2024)
- [Financial Report Chunking for Effective Retrieval Augmented Generation](https://mlsanity.com/paper/2402.05131/) (2024)
- [Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks](https://mlsanity.com/paper/2407.21059/) (2024)
- [Swiss Cheese Model for AI Safety: A Taxonomy and Reference Architecture for Multi-Layered Guardrails of Foundation Model Based Agents](https://mlsanity.com/paper/2408.02205/) (2024)
- [A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Models](https://mlsanity.com/paper/2311.11415/) (2023)
- [Greenback Bears and Fiscal Hawks: Finance is a Jungle and Text Embeddings Must Adapt](https://mlsanity.com/paper/2411.07142/) (2024)
- [Optimizing Query Generation for Enhanced Document Retrieval in RAG](https://mlsanity.com/paper/2407.12325/) (2024)
- [Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge](https://mlsanity.com/paper/2411.16732/) (2024)
- [FCMR: Robust Evaluation of Financial Cross-Modal Multi-Hop Reasoning](https://mlsanity.com/paper/2412.12567/) (2024)
- [Domain-Specific Pretraining of Language Models: A Comparative Study in the Medical Field](https://mlsanity.com/paper/2407.14076/) (2024)
- [Improving embedding with contrastive fine-tuning on small datasets with expert-augmented scores](https://mlsanity.com/paper/2408.11868/) (2024)
- [Towards Efficient Methods in Medical Question Answering using Knowledge Graph Embeddings](https://mlsanity.com/paper/2401.07977/) (2024)

## Built on by (most cited first, 9 of 9)

- [RAGXplain: From Explainable Evaluation to Actionable Guidance of RAG Pipelines](https://mlsanity.com/paper/2505.13538/) (2025)
- [VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering](https://mlsanity.com/paper/2510.10828/) (2025)
- [IMRNNs: An Efficient Method for Interpretable Dense Retrieval via Embedding Modulation](https://mlsanity.com/paper/2601.20084/) (2026)
- [MAFA: A multi-agent framework for annotation](https://mlsanity.com/paper/2505.13668/) (2025)
- [Benchmarking Filtered Approximate Nearest Neighbor Search Algorithms on Transformer-based Embedding Vectors](https://mlsanity.com/paper/2507.21989/) (2025)
- [Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3](https://mlsanity.com/paper/2506.16037/) (2025)
- [Decomposing Retrieval Failures in RAG for Long-Document Financial Question Answering](https://mlsanity.com/paper/2602.17981/) (2026)
- [A Picture is Worth a Thousand Words? An Empirical Study of Aggregation Strategies for Visual Financial Document Retrieval](https://mlsanity.com/paper/2605.14581/) (2026)
- [MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration](https://mlsanity.com/paper/2605.25030/) (2026)

## Abstract (arXiv)

Retrieval-Augmented Generation (RAG) has emerged as a promising framework to mitigate hallucinations in Large Language Models (LLMs), yet its overall performance is dependent on the underlying retrieval system. In the finance domain, documents such as 10-K reports pose distinct challenges due to domain-specific vocabulary and multi-hierarchical tabular data. In this work, we introduce an efficient, end-to-end RAG pipeline that enhances retrieval for financial documents through a three-phase approach: pre-retrieval, retrieval, and post-retrieval. In the pre-retrieval phase, various query and corpus preprocessing techniques are employed to enrich input data. During the retrieval phase, we fine-tuned state-of-the-art (SOTA) embedding models with domain-specific knowledge and implemented a hybrid retrieval strategy that combines dense and sparse representations. Finally, the post-retrieval phase leverages Direct Preference Optimization (DPO) training and document selection methods to further refine the results. Evaluations on seven financial question answering datasets-FinDER, FinQABench, FinanceBench, TATQA, FinQA, ConvFinQA, and MultiHiertt-demonstrate substantial improvements in retrieval performance, leading to more accurate and contextually appropriate generation. These findings highlight the critical role of tailored retrieval techniques in advancing the effectiveness of RAG systems for financial applications. A fully replicable pipeline is available on GitHub: https://github.com/seohyunwoo-0407/GAR.