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

View PDF HTML (experimental) 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 Exams, Mina scored 75-80% in Preliminary MCQs, Written, and simulated Viva Voce exams, matching or surpassing average human performance and demonstrating clarity, contextual understanding, and sound legal reasoning. Even under a conservative upper bound, Mina operates at just 0.12-0.61% of typical legal consultation costs in Bangladesh, yielding a 99.4-99.9\% cost reduction relative to human-provided services. 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 case study on building domain-specific, low-resource systems and addressing challenges of multilingual adaptation, efficiency, and sustainable public-service AI deployment. Comments: Accepted to ACL 2026 Findings Subjects: Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA); Multimedia (cs.MM) Cite as: arXiv:2511.08605 [cs.CL] (or arXiv:2511.08605v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2511.08605 arXiv-issued DOI via DataCite Submission history From: Azmine Toushik Wasi [view email] [v1] Tue, 4 Nov 2025 20:43:28 UTC (3,164 KB) [v2] Fri, 28 Nov 2025 14:20:39 UTC (3,165 KB) [v3] Thu, 9 Apr 2026 14:39:20 UTC (2,389 KB)