Papers by Nabeel Mohammed
BanLemma: A Word Formation Dependent Rule and Dictionary Based Bangla Lemmatizer (2023.findings-emnlp)
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Sadia Afrin, Md. Shahad Mahmud Chowdhury, Md. Islam, Faisal Khan, Labib Chowdhury, Md. Mahtab, Nazifa Chowdhury, Massud Forkan, Neelima Kundu, Hakim Arif, Mohammad Mamun Or Rashid, Mohammad Amin, Nabeel Mohammed
| Challenge: | Lemmatization holds significance in both natural language processing (NLP) and linguistics due to the highly inflected nature and morphological richness of Bangla text. |
| Approach: | They propose linguistic rules for lemmatization and utilize a dictionary along with the rules to design a lemma specifically for Bangla. |
| Outcome: | The proposed system achieves 96.36% accuracy when tested against a manually annotated test dataset. |
Bidirectional Reasoning Supervision for Multilingual Financial Decision Making (2025.emnlp-industry)
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Muhammad Rafsan Kabir, Jawad Ibn Ahad, Robin Krambroeckers, Silvia Ahmed, M M Lutfe Elahi, Nabeel Mohammed, Shafin Rahman
| Challenge: | Large Language Models have been used for sentiment analysis, machine translation, and question answering, but their effectiveness in the multilingual financial domain remains unknown. |
| Approach: | They propose a fine-tuning approach that integrates positive and negative rationales alongside classification labels. |
| Outcome: | The proposed approach outperforms existing methods across English, Hindi, Bengali, and Telugu, and is suitable for industry applications. |
Adaptive Weighted Proxy Tuning: Efficient Gray-Box Steering for Image Captioning. (2026.acl-industry)
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| Challenge: | Proxy tuning is a decoding-time approach that fails to account for instance-specific variations in model certainty and domain shift. |
| Approach: | They propose a gray-box steering framework that dynamically modulates the logit contributions of a large base model, a fine-tuned expert, and an untune . |
| Outcome: | Adaptive Weighted Proxy Tuning achieves performance parity with fine-tuned models while remaining parameter-free. |
CAPSTONE: Composable Attribute‐Prompted Scene Translation for Zero‐Shot Vision–Language Reasoning (2025.emnlp-industry)
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| Challenge: | CAPSTONE transforms visual inputs into structured text prompts that can be interpreted by a frozen Large Language Model (LLM). |
| Approach: | They propose a plug-and-play framework that transforms off-the-shelf vision models into structured text prompts that can be interpreted by a frozen Large Language Model (LLM). |
| Outcome: | The proposed framework outperforms fully trained VLMs on the POPE dataset while the 4B model achieves competitive results. |
BanSuite: A Unified Toolkit and Software Platform for Low-Resource NLP in Bangla (2026.eacl-demo)
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Md. Abu Sayed, Faisal Ahamed Khan, Jannatul Ferdous Tuli, Nabeel Mohammed, Mohammad Ruhul Amin, Mohammad Mamun Or Rashid
| Challenge: | Existing efforts to improve Bangla's NLP performance have focused on isolated tasks such as Part-of-Speech tagging and Named Entity Recognition (NER) but comprehensive, integrated systems for core NLP tasks such Shallow Parsing and Dependency Parser are largely absent. |
| Approach: | They propose to integrate a large-scale, manually annotated Bangla Treebank with high-quality pretrained models for POS tagging, NER, shallow parsing, and dependency parse. |
| Outcome: | The proposed system achieves strong in-domain baseline performance while maintaining high efficiency in resource usage. |
AutoDSPy: Automating Modular Prompt Design with Reinforcement Learning for Small and Large Language Models (2025.emnlp-industry)
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Nafew Azim, Abrar Ur Alam, Hasan Bin Omar, Abdullah Mohammad Muntasir Adnan Jami, Jawad Ibn Ahad, Muhammad Rafsan Kabir, Md. Ismail Hossain, Fuad Rahman, Mohammad Ruhul Amin, Shafin Rahman, Nabeel Mohammed
| Challenge: | Large Language Models excel at complex reasoning tasks, yet their performance hinges on the quality of their prompts and pipeline structures. |
| Approach: | They propose a framework that fully automates large language models' pipeline construction using reinforcement learning. |
| Outcome: | Experimental results show that autoDSPy outperforms DSPy benchmarks in accuracy gains and time. |
BanNERD: A Benchmark Dataset and Context-Driven Approach for Bangla Named Entity Recognition (2025.findings-naacl)
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Md. Motahar Mahtab, Faisal Ahamed Khan, Md. Ekramul Islam, Md. Shahad Mahmud Chowdhury, Labib Imam Chowdhury, Sadia Afrin, Hazrat Ali, Mohammad Mamun Or Rashid, Nabeel Mohammed, Mohammad Ruhul Amin
| Challenge: | In a cross-dataset evaluation, models trained on BanNERD consistently outperformed those trained on four existing Bangla NER datasets. |
| Approach: | They propose to use Bangla as a language to create the most extensive human-annotated and validated Bangla NLP dataset. |
| Outcome: | The proposed method outperforms existing methods on Bangla NER datasets and performs competitively on English datasets. |