RIRAG : A Bi-Directional Retrieval-Enhanced Framework for Financial Legal QA in O bli QA Shared Task (2025)

Abstract: In professional financial-legal consulting services, accurately and efficiently retrieving and answering legal questions is crucial. Although some breakthroughs have been made in information retrieval and answer generation, few frameworks have successfully integrated these tasks. Therefore, we propose RIRAG (Retrieval-In-the-loop Response and Answer Generation), a bi-directional retrieval-enhanced framework for financial-legal question answering in ObliQA Shared Task. The system introduces BDD-FinLegal, which means Bi-Directional Dynamic finance-legal, a novel retrieval mechanism specifically designed for financial-legal documents, combining traditional retrieval algorithms with modern neural network methods. Legal answer generation is implemented through large language models retrained on expert-annotated datasets. Our method significantly improves the professionalism and interpretability of the answers while maintaining high retrieval accuracy. Experiments on the ADGM dataset show that the system achieved a significant improvement in the Recall@10 evaluation metric and was recognized by financial legal experts for the accuracy and professionalism of the answer generation. This study provides new ideas for building efficient and reliable question-answering systems in the financial-legal domain.

## introduction financial-legal question answering systems have emerged as crucial tools for improving access to specialized legal information and services in the financial sector. the complexity of financial-legal documents, combined with the need for accurate and context-aware responses, presents unique challenges in natural language processing. this paper introduces rirag, a hybrid system that combines our novel bdd-finlegal retrieval mecha-nism, cross-encoding, and advanced language models specifically trained for financial-legal domain question answering. recent advances in large language models have revolutionized question answering systems, yet their application in the financial-legal domain remains challenging due to the need for precise citation and adherence to financial regulatory frameworks. previous approaches have either focused solely on retrieval accuracy or generation quality, often failing to maintain a balance between both aspects. therefore, we have constructed a completely new system and employed innovative models to address the aforementioned issues. in brief, the contributions of our work are as follows: the rest of this paper is structured as follows: section 2 provides a comprehensive review of existing research on question answering and retrieval systems, identifying key challenges in the domain. section 3 details our methodology and system architecture, including the novel bdd-finlegal mechanism. section 4 presents the experimental results and analysis. section 5 discusses the implications and limitations, and section 6 concludes. ## methodology this section details the architecture and implementation of our rirag system, comprising three main components: bdd-finlegal retrieval, cross-encoder reranking, and expert-retrained answer generation. ## experiments and results this section presents our experimental setup, evaluation metrics, and comparative analysis of different retrieval and generation approaches. ## conclusion in this paper, we introduced rirag, a bidirectional retrieval-enhanced framework for financial-legal question answering in obliqa shared task. our key contributions are a dynamic bdd-finlegal retrieval mechanism adapting strategies based on query characteristics, a specialized cross-encoder reranking approach enhancing passage relevance, and an expert-retrained answer generation framework maintaining high professional standards. experimental results on the adgm financial-legal dataset showed significant improvements in retrieval accuracy, answer quality, and expert evaluation metrics, with a recall@10 of 0.759 and an expert evaluation score of 0.834, outperforming existing approaches. our work provides a promising direction for developing more accurate, interpretable, and reliable question-answering systems in the financiallegal domain. by combining advanced retrieval techniques, neural reranking, and domain-specific language models, we have addressed critical challenges in legal information access.