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    <abstract>Bangladesh’s low-income population faces major barriers to affordable legal advice due to complex legal language, procedural opacity, and high costs. Existing AI legal assistants lack Bengali-language support and jurisdiction-specific adaptation, limiting their effectiveness. To address this, we developed Mina, a multilingual LLM-based legal assistant tailored for the Bangladeshi context. It employs multilingual embeddings and a RAG-based chain-of-tools framework for retrieval, reasoning, translation, and document generation, delivering context-aware legal drafts, citations, and plain-language explanations via an interactive chat interface. Evaluated by law faculty from leading Bangladeshi universities across all stages of the 2022 and 2023 Bangladesh Bar Council examinations, Mina achieved scores of 75–80% in the preliminary MCQs, written, and simulated viva voce components. These results matched or surpassed average human performance, demonstrating strong clarity, contextual understanding, and sound legal reasoning, while operating at approximately 0.1-0.6% of the cost of human lawyers. These results confirm its potential as a low-cost, multilingual AI assistant that automates key legal tasks and scales access to justice, offering a real-world details on building domain-specific, low-resource systems and addressing challenges of multilingual adaptation, efficiency, and sustainable public-service AI deployment.</abstract>
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%0 Conference Proceedings
%T Mina: A Multilingual LLM-Powered Legal Assistant Agent for Empowering Access to Justice in Bangladesh
%A Wasi, Azmine Toushik
%A Faisal, Wahid
%A Islam, Mst Rafia
%A Parvez, Md Rizwan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F wasi-etal-2026-mina
%X Bangladesh’s low-income population faces major barriers to affordable legal advice due to complex legal language, procedural opacity, and high costs. Existing AI legal assistants lack Bengali-language support and jurisdiction-specific adaptation, limiting their effectiveness. To address this, we developed Mina, a multilingual LLM-based legal assistant tailored for the Bangladeshi context. It employs multilingual embeddings and a RAG-based chain-of-tools framework for retrieval, reasoning, translation, and document generation, delivering context-aware legal drafts, citations, and plain-language explanations via an interactive chat interface. Evaluated by law faculty from leading Bangladeshi universities across all stages of the 2022 and 2023 Bangladesh Bar Council examinations, Mina achieved scores of 75–80% in the preliminary MCQs, written, and simulated viva voce components. These results matched or surpassed average human performance, demonstrating strong clarity, contextual understanding, and sound legal reasoning, while operating at approximately 0.1-0.6% of the cost of human lawyers. These results confirm its potential as a low-cost, multilingual AI assistant that automates key legal tasks and scales access to justice, offering a real-world details on building domain-specific, low-resource systems and addressing challenges of multilingual adaptation, efficiency, and sustainable public-service AI deployment.
%U https://aclanthology.org/2026.findings-acl.1295/
%P 25980-26028

Markdown (Informal)

[Mina: A Multilingual LLM-Powered Legal Assistant Agent for Empowering Access to Justice in Bangladesh](https://aclanthology.org/2026.findings-acl.1295/) (Wasi et al., Findings 2026)

• Mina: A Multilingual LLM-Powered Legal Assistant Agent for Empowering Access to Justice in Bangladesh (Wasi et al., Findings 2026)

ACL

• Azmine Toushik Wasi, Wahid Faisal, Mst Rafia Islam, and Md Rizwan Parvez. 2026. Mina: A Multilingual LLM-Powered Legal Assistant Agent for Empowering Access to Justice in Bangladesh. In Findings of the Association for Computational Linguistics: ACL 2026, pages 25980–26028, San Diego, California, United States. Association for Computational Linguistics.