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.

Hate speech detection in multilingual and code-mixed contexts remains a significant challenge due to linguistic diversity and overlapping syntactic structures. This paper presents a study on the detection of hate speech in Tamil and Malayalam using transformer-based models. Our goal is to address underfitting and develop effective models for hate speech classification. We evaluate several pre-trained models, including MuRIL and XLM-RoBERTa, and show that fine-tuning is crucial for better performance. The test results show a Macro-F1 score of 0.7039 for Tamil and 0.6402 for Malayalam, highlighting the promise of these models with further improvements in fine-tuning. We also discuss data preprocessing techniques, model implementations, and experimental findings. Our full experimental codebase is publicly available at: github.com/ciol-researchlab/NAACL25-NLPops-Classification-Abusive-Text.

Integrating cognitive ergonomics with LLMs is crucial for improving safety, reliability, and user satisfaction in human-AI interactions. Current LLM designs often lack this integration, resulting in systems that may not fully align with human cognitive capabilities and limitations. This oversight exacerbates biases in LLM outputs and leads to suboptimal user experiences due to inconsistent application of user-centered design principles. Researchers are increasingly leveraging NLP, particularly LLMs, to model and understand human behavior across social sciences, psychology, psychiatry, health, and neuroscience. Our position paper explores the need to integrate cognitive ergonomics into LLM design, providing a comprehensive framework and practical guidelines for ethical development. By addressing these challenges, we aim to advance safer, more reliable, and ethically sound human-AI interactions.