Research Experience
My research focuses on Natural Language Processing for low-resource languages, multimodal AI, and reasoning systems. I have published at EMNLP and NAACL workshops, with a record of competitive performance in shared tasks.
Code Generation in Bangla: Low-Resource Language Adaptation
2025 | Shared Task at the BLP Workshop, co-located with IJCNLP-AACL
🏆 4th Place out of 32 Teams — 85% Accuracy
- Investigated code generation in Bangla by fine-tuning open-source models (Llama 3, TigerLLM, Qwen) using parameter-efficient LoRA adapters to overcome data scarcity.
- Enhanced reasoning via Chain-of-Thought (CoT) prompting and a self-refinement loop that iteratively critiques and corrects generated code based on execution feedback.
- Conducted rigorous error analysis to identify failure modes in code generation tasks.
- Publication: AdversaryAI at BLP-2025 Task 2: A Think, Refine, and Generate (TriGen) System with LoRA and Self-Refinement for Code Generation
BRAINTEASER: Advanced Commonsense Reasoning in Language Models
2024 | Shared Task at SemEval 2024, co-located with NAACL
- Designed data augmentation pipelines to improve model robustness for complex commonsense reasoning tasks involving lateral thinking puzzles.
- Conducted a comparative analysis of advanced language models, analyzing performance gaps and reasoning patterns in non-standard logical scenarios.
- Publication: Deja Vu at SemEval 2024 Task 9: A Comparative Study of Advanced Language Models for Commonsense Reasoning
Violence Inciting Text Detection (VITD) in Bangla
2023 | Shared Task at the BLP Workshop, co-located with EMNLP
📊 Top 20 — Improved from Rank 19 → 12 in Post-evaluation
- Applied semi-supervised self-training to address class imbalance, significantly improving performance on minority classes.
- Enhanced dataset diversity through back-translation using the Googletrans API, improving semantic variety while correcting linguistic inconsistencies.
- Implemented an ensemble approach combining multiple transformer models with bagging and majority voting to reduce prediction variance.
- Publication: Team_Syrax at BLP-2023 Task 1: Data Augmentation and Ensemble Based Approach for Violence Inciting Text Detection in Bangla
Improving Answer Space Diversity in Visual Question Answering (VQA)
2022 | Undergraduate Thesis Project
- Conducted a comparative study of VQA methods, identifying core limitations in answer distribution.
- Addressed the “Answer Space Diversity” limitation by augmenting training data with automatically generated, contextually relevant QA pairs using template-based synthesis.
- Demonstrated improved performance on long-tail answer categories, reducing model bias toward frequent answers.