LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913804161384448 |
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| author | Kabir, Muhammad Rafsan Sultan, Rafeed Mohammad Rahman, Fuad Amin, Mohammad Ruhul Momen, Sifat Mohammed, Nabeel Rahman, Shafin |
| author_facet | Kabir, Muhammad Rafsan Sultan, Rafeed Mohammad Rahman, Fuad Amin, Mohammad Ruhul Momen, Sifat Mohammed, Nabeel Rahman, Shafin |
| contents | Natural Language Processing (NLP) and computational linguistic techniques are increasingly being applied across various domains, yet their use in legal and regulatory tasks remains limited. To address this gap, we develop an efficient bilingual question-answering framework for regulatory documents, specifically the Bangladesh Police Gazettes, which contain both English and Bangla text. Our approach employs modern Retrieval Augmented Generation (RAG) pipelines to enhance information retrieval and response generation. In addition to conventional RAG pipelines, we propose an advanced RAG-based approach that improves retrieval performance, leading to more precise answers. This system enables efficient searching for specific government legal notices, making legal information more accessible. We evaluate both our proposed and conventional RAG systems on a diverse test set on Bangladesh Police Gazettes, demonstrating that our approach consistently outperforms existing methods across all evaluation metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16121 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval Kabir, Muhammad Rafsan Sultan, Rafeed Mohammad Rahman, Fuad Amin, Mohammad Ruhul Momen, Sifat Mohammed, Nabeel Rahman, Shafin Information Retrieval Computation and Language Machine Learning Natural Language Processing (NLP) and computational linguistic techniques are increasingly being applied across various domains, yet their use in legal and regulatory tasks remains limited. To address this gap, we develop an efficient bilingual question-answering framework for regulatory documents, specifically the Bangladesh Police Gazettes, which contain both English and Bangla text. Our approach employs modern Retrieval Augmented Generation (RAG) pipelines to enhance information retrieval and response generation. In addition to conventional RAG pipelines, we propose an advanced RAG-based approach that improves retrieval performance, leading to more precise answers. This system enables efficient searching for specific government legal notices, making legal information more accessible. We evaluate both our proposed and conventional RAG systems on a diverse test set on Bangladesh Police Gazettes, demonstrating that our approach consistently outperforms existing methods across all evaluation metrics. |
| title | LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval |
| topic | Information Retrieval Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2504.16121 |