Mina: A Multilingual LLM-Powered Legal Assistant Agent for Empowering Access to Justice in Bangladesh

ACL ARR 2026 January Submission5512 Authors

Keywords: legal AI, multilingual large language models, retrieval-augmented generation, low-resource languages, access to justice, domain-specific NLP, public-sector AI

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 \textsc{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, \textsc{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.

Paper Type: Long

Research Area: Dialogue and Interactive Systems

Research Area Keywords: multilingual / low resource; applications; legal NLP, NLP for social good; less-resourced languages; LLM/AI agents; valuation and metrics; task-oriented; human-in-the-loop; bias/toxicity; factuality; retrieval; knowledge augmented;

Contribution Types: NLP engineering experiment, Approaches low compute settings-efficiency, Publicly available software and/or pre-trained models

Languages Studied: Bengali, English

Submission Number: 5512