# Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge

arXiv: https://arxiv.org/abs/2411.16732 · HTML: https://mlsanity.com/paper/2411.16732/

## Key facts

- Cited by 10 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 3 of its 9 references that are in the map.
- First posted on arXiv 23 Nov 2024.

## Placed in

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

## Builds on (most cited first, 3 of 3)

- [Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG](https://mlsanity.com/paper/2410.05983/) (2024)
- [Improving Retrieval for RAG based Question Answering Models on Financial Documents](https://mlsanity.com/paper/2404.07221/) (2024)
- [Optimizing Query Generation for Enhanced Document Retrieval in RAG](https://mlsanity.com/paper/2407.12325/) (2024)

## Built on by (most cited first, 10 of 10)

- [R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2503.05592/) (2025)
- [StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization](https://mlsanity.com/paper/2505.15107/) (2025)
- [Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2503.15191/) (2025)
- [Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization](https://mlsanity.com/paper/2604.14267/) (2026)
- [VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering](https://mlsanity.com/paper/2510.10828/) (2025)
- [Point-in-Time Financial RAG with Frozen LLMs and Market-Feedback Adaptive Retrieval](https://mlsanity.com/paper/2605.31201/) (2026)
- [History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecasting](https://mlsanity.com/paper/2511.09754/) (2025)
- [MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration](https://mlsanity.com/paper/2605.25030/) (2026)
- [Sifei at SemEval-2026 Task 8: Hybrid Retrieval and Query Rewriting for Multi-Turn RAG](https://mlsanity.com/paper/2606.28352/) (2026)
- [IGT @ FinMMEval 2026 Task 2: Question-Type Prompting with Targeted Extraction for Multilingual Financial QA](https://mlsanity.com/paper/2609.08139/) (2026)

## Abstract (arXiv)

As Large Language Models (LLMs) increasingly address domain-specific problems, their application in the financial sector has expanded rapidly. Tasks that are both highly valuable and time-consuming, such as analyzing financial statements, disclosures, and related documents, are now being effectively tackled using LLMs. This paper details the development of a high-performance, finance-specific Retrieval-Augmented Generation (RAG) system for the ACM-ICAIF '24 FinanceRAG competition. We optimized performance through ablation studies on query expansion and corpus refinement during the pre-retrieval phase. To enhance retrieval accuracy, we employed multiple reranker models. Notably, we introduced an efficient method for managing long context sizes during the generation phase, significantly improving response quality without sacrificing performance. We ultimately achieve 2nd place in the FinanceRAG Challenge. Our key contributions include: (1) pre-retrieval ablation analysis, (2) an enhanced retrieval algorithm, and (3) a novel approach for long-context management. This work demonstrates the potential of LLMs in effectively processing and analyzing complex financial data to generate accurate and valuable insights. The source code and further details are available at https://github.com/cv-lee/FinanceRAG.