Mina用多语言大模型为孟加拉低收入人群提供低成本法律助手，解决语言和费用难题。
Mina: A Multilingual LLM-Powered Legal Assistant Agent for Bangladesh for Empowering Access to Justice
- 基于多语言嵌入与RAG工具链，实现法律检索、推理、翻译与文书生成
- 在孟加拉律师资格考试中表现达75-80%，接近或超越人类平均水平
- 成本仅为人工服务的0.12%-0.61%，适合低资源地区普惠法律服务
孟加拉低收入群体因法律语言复杂、程序不透明及高昂费用，难以获得可负担的法律咨询。现有AI法律助手缺乏孟加拉语支持和本地化适配，效果有限。为此，我们开发了Mina——一款面向孟加拉国情的多语言大模型法律助手。它采用多语言嵌入与基于RAG的工具链框架，实现检索、推理、翻译与文书生成，通过交互式聊天界面提供上下文相关的法律草案、引用及通俗解释。由孟加拉顶尖大学法学院教师在2022年与2023年巴里克委员会考试的初试选择题、笔试及模拟口试各阶段评估，Mina得分75-80%，达到或超过人类平均表现，展现出清晰表达、上下文理解与合理法律推理能力。即使按保守上限计算，其运行成本仅为人工作业的0.12%-0.61%，相较人工服务节省99.4%-99.9%成本。结果证实，Mina具备作为低成本、多语言、自动化法律任务执行工具的潜力，可拓展司法可及性，为构建领域特定、低资源系统提供了真实世界范例，解决了多语言适配、效率与可持续公共AI部署等挑战。
原文摘要 · Abstract (English)
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.
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