95 citations · 117 across the 17 of their papers we have counts for
19 papers
BanglaShield: A Script-Robust Safety Classifier and Benchmark for Bangla Native, Romanized, and Code-Mixed Prompts
Mohsinul Kabir, Md. Tariquzzaman, Mohammad Rashidujjaman Rifat +2
Safety classifiers for large language models (LLMs) are typically built and evaluated on standard-script text, yet Bangla, like many lower-resource languages, is routinely written…
In the Guise of Correction: How Framing and Cultural Adaptation Erode LLM Safety on Real-World Criminal Cases
Mohsinul Kabir, Md. Tariquzzaman, Karam S. R. Elradie +4
Existing safety benchmarks rely largely on synthetic prompts with loosely grounded taxonomies and offer little focused analysis of severe crime categories. We introduce GUISE, a be…
Right Frame, Wrong Rule: Cultural Cues Expose the Financial Knowledge Gap They Were Meant to Close
Rania Elbadry, Ahmed Heakl, Saeed Almheiri +12
When a question has valid answers under different normative frameworks, a language model must decide which framework to use and whether it can answer correctly within it. We call t…
Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images
Yuechen Jiang, Enze Zhang, Md Mohsinul Kabir +4
Recent advances in vision-language models (VLMs) have improved image captioning for cultural heritage. However, inferring structured cultural metadata (e.g., creator, origin, perio…
XCR-Bench: Benchmarking Cross-Cultural Reasoning in LLMs via Culture-Specific Items and Hall's Triad
Mohsinul Kabir, Tasnim Ahmed, Md Mezbaur Rahman +5
Cross-cultural competence in large language models (LLMs) requires understanding and adapting Culture-Specific Items (CSIs) across varying cultural contexts. However, progress in e…
Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection
Zhiwei Liu, Yupen Cao, Yuechen Jiang +22
Large language models (LLMs) have been widely applied across various domains of finance. Since their training data are largely derived from human-authored corpora, LLMs may inherit…
Semantic Label Drift in Cross-Cultural Translation
Mohsinul Kabir, Tasnim Ahmed, Md Mezbaur Rahman +2
Machine Translation (MT) is widely employed to address resource scarcity in low-resource languages by generating synthetic data from high-resource counterparts. While sentiment pre…
From n-gram to Attention: How Model Architectures Learn and Propagate Bias in Language Modeling
Mohsinul Kabir, Tasfia Tahsin, Sophia Ananiadou
Current research on bias in language models (LMs) predominantly focuses on data quality, with significantly less attention paid to model architecture and temporal influences of dat…
Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs
Mohsinul Kabir, Ajwad Abrar, Sophia Ananiadou
A large number of studies rely on closed-style multiple-choice surveys to evaluate cultural alignment in Large Language Models (LLMs). In this work, we challenge this constrained e…
Religious Bias Landscape in Language and Text-to-Image Models: Analysis, Detection, and Debiasing Strategies
Ajwad Abrar, Nafisa Tabassum Oeshy, Mohsinul Kabir +1
Note: This paper includes examples of potentially offensive content related to religious bias, presented solely for academic purposes. The widespread adoption of language models hi…
BDA: Bangla Text Data Augmentation Framework
Md. Tariquzzaman, Audwit Nafi Anam, Naimul Haque +3
Data augmentation involves generating synthetic samples that resemble those in a given dataset. In resource-limited fields where high-quality data is scarce, augmentation plays a c…
Islamic Lifestyle Applications: Meeting the Spiritual Needs of Modern Muslims
Mohsinul Kabir, Mohammad Ridwan Kabir, Riasat Siam Islam
We evaluated contemporary Islamic lifestyle applications supporting religious practices and motivation among Muslims. We reviewed 11 popular applications using self-determination t…
Towards Automated Recipe Genre Classification using Semi-Supervised Learning
Nazmus Sakib, G. M. Shahariar, Md. Mohsinul Kabir +2
Sharing cooking recipes is a great way to exchange culinary ideas and provide instructions for food preparation. However, categorizing raw recipes found online into appropriate foo…
Math Word Problem Solving by Generating Linguistic Variants of Problem Statements
Syed Rifat Raiyan, Md. Nafis Faiyaz, Shah Md. Jawad Kabir +3
The art of mathematical reasoning stands as a fundamental pillar of intellectual progress and is a central catalyst in cultivating human ingenuity. Researchers have recently publis…
"When Words Fail, Emojis Prevail": Generating Sarcastic Utterances with Emoji Using Valence Reversal and Semantic Incongruity
Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir +3
Sarcasm is a form of figurative language that serves as a humorous tool for mockery and ridicule. We present a novel architecture for sarcasm generation with emoji from a non-sarca…
BanglaBook: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews
Mohsinul Kabir, Obayed Bin Mahfuz, Syed Rifat Raiyan +2
The analysis of consumer sentiment, as expressed through reviews, can provide a wealth of insight regarding the quality of a product. While the study of sentiment analysis has been…
Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning
Nazmus Sakib, G. M. Shahariar, Md. Mohsinul Kabir +2
Cooking recipes allow individuals to exchange culinary ideas and provide food preparation instructions. Due to a lack of adequate labeled data, categorizing raw recipes found onlin…
DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities
Mohsinul Kabir, Tasnim Ahmed, Md. Bakhtiar Hasan +4
Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data. In this study, we leverage the clinical articulat…
Computational Sarcasm Analysis on Social Media: A Systematic Review
Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir +3
Sarcasm can be defined as saying or writing the opposite of what one truly wants to express, usually to insult, irritate, or amuse someone. Because of the obscure nature of sarcasm…