Papers by Mohsinul Kabir
Math Word Problem Solving by Generating Linguistic Variants of Problem Statements (2023.acl-srw)
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Syed Rifat Raiyan, Md Nafis Faiyaz, Shah Md. Jawad Kabir, Mohsinul Kabir, Hasan Mahmud, Md Kamrul Hasan
| Challenge: | Existing models for solving Math Word Problems depend on shallow heuristics and spurious correlations to derive the solution expressions. |
| Approach: | They propose a framework for MWP solvers based on generation of linguistic variants of problem text. |
| Outcome: | The proposed framework improves the mathematical reasoning and robustness of the proposed model. |
From n-gram to Attention: How Model Architectures Learn and Propagate Bias in Language Modeling (2025.findings-emnlp)
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| Challenge: | Current research on bias in language models focuses on data quality, not temporal influences of data. |
| Approach: | They propose a methodology to interpret the interaction between training data and model architecture in bias propagation during language modeling. |
| Outcome: | The proposed method analyzes the interaction between training data and model architecture in bias propagation during language modeling. |
Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection (2026.findings-acl)
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Zhiwei Liu, Yupeng Cao, Yuechen Jiang, Mohsinul Kabir, Polydoros Giannouris, Chen Xu, Ziyang Xu, Tianlei Zhu, Md. Tariquzzaman, Triantafillos Papadopoulos, Yan Wang, Lingfei Qian, Xueqing Peng, Zhuohan Xie, Ye Yuan, Saeed Almheiri, Abdulrazzaq Alnajjar, Ming-Bin Chen, Harry Stuart, Paul Thompson, Prayag Tiwari, Alejandro Lopez-Lira, Xue Liu, Jimin Huang, Sophia Ananiadou
| Challenge: | Existing research on LLM biases has focused on direct questioning or general-purpose settings . pronounced behavioral biase despite their growing deployment in financial analysis, forecasting, and decision support. |
| Approach: | They propose a benchmark to evaluate behavioral biases of large language models in MFMD . they use a multilingual financial misinformation dataset to integrate these with misinformation claims . |
| Outcome: | The proposed benchmark evaluates behavioral biases of large language models across economic scenarios. |
BanglaBook: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews (2023.findings-acl)
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| Challenge: | Existing literature on Bangla Sentiment Analysis (SA) has limited data and cross-domain adaptability. |
| Approach: | They present a large-scale dataset of Bangla book reviews with 158,065 samples . they employ a range of machine learning models to establish baselines including SVM, LSTM, and Bangla-BERT. |
| Outcome: | The proposed model improves performance over models that rely on manual features. |
Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs (2025.emnlp-main)
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| Challenge: | a large number of studies rely on closed-style multiple-choice surveys to evaluate cultural alignment in Large Language Models . however, these methods are constrained and lack nuanced and accurate evaluations based on specific cultural proxies. |
| Approach: | They propose to use the World Values Survey and Hofstede Cultural Dimensions as case studies to examine cultural alignment in Large Language Models. |
| Outcome: | The findings advocate for more robust evaluation frameworks that focus on cultural proxies. |
“When Words Fail, Emojis Prevail”: A Novel Architecture for Generating Sarcastic Sentences With Emoji Using Valence Reversal and Semantic Incongruity (2023.acl-srw)
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Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, Mohsinul Kabir, Hasan Mahmud, Md Kamrul Hasan
| Challenge: | Existing sarcasm generation tasks focus on textual sarcasm, but people often use emojis to express their emotions. |
| Approach: | They propose a novel architecture for sarcasm generation with emojis from a non-sarcastic input sentence in English. |
| Outcome: | The proposed architecture generates sarcastic outputs with emojis from a non-sarcastic input sentence in english. |
BenLLM-Eval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP (2024.lrec-main)
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Mohsinul Kabir, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Mir Tafseer Nayeem, M Saiful Bari, Enamul Hoque
| Challenge: | Large Language Models (LLMs) have emerged as one of the most important breakthroughs in natural language processing. |
| Approach: | They propose to evaluate LLMs in Bengali to benchmark their performance . they select Bangla NLP tasks such as text summarization, question answering, paraphrasing . |
| Outcome: | The proposed model performs better in some tasks than current models, but in most tasks, it is poor . |