Papers by Mir Tafseer Nayeem
Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning (2024.findings-emnlp)
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Mohammed Saidul Islam, Raian Rahman, Ahmed Masry, Md Tahmid Rahman Laskar, Mir Tafseer Nayeem, Enamul Hoque
| Challenge: | Recent studies have demonstrated that large vision language models (LVLMs) are not multi-modal and lack multi-tasking capabilities. |
| Approach: | They evaluate the performance of large vision language models (LVLMs) for chart understanding and reasoning tasks and compare them to open-source models. |
| Outcome: | The proposed models demonstrate impressive abilities in generating fluent texts covering high-level data insights, but they also encounter common problems like hallucinations, factual errors, and data bias. |
On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction (2023.findings-eacl)
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| Challenge: | Existing methods for detecting helpful reviews focus on review text and ignore the two key factors of (1) who post the reviews and (2) when the reviews are posted. |
| Approach: | They propose to integrate reviewer's expertise and temporal dynamics to predict helpfulness for unreliable and cold-start reviews. |
| Outcome: | The proposed model improves on existing models and compares with baselines. |
Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning? (2025.acl-industry)
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Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Ridwan Mahbub, Ahmed Masry, Mizanur Rahman, Amran Bhuiyan, Mir Tafseer Nayeem, Shafiq Joty, Enamul Hoque, Jimmy Huang
| Challenge: | Large Vision-Language Models (LVLMs) are expensive and time-consuming to evaluate . however, they are limited in their use in industrial settings due to their limited availability and limited resources. |
| Approach: | They evaluate 13 open-source LVLMs as judges for diverse chart comprehension and reasoning tasks. |
| Outcome: | The proposed models can be used to assess chart comprehension and reasoning tasks, but they are expensive and time-consuming. |
Unsupervised Abstractive Summarization of Bengali Text Documents (2021.eacl-main)
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Radia Rayan Chowdhury, Mir Tafseer Nayeem, Tahsin Tasnim Mim, Md. Saifur Rahman Chowdhury, Taufiqul Jannat
| Challenge: | Abstractive summarization systems are difficult to perform due to the unavailability of the parallel data for low-resource languages like Bengali. |
| Approach: | They propose a graph-based unsupervised abstractive summarization system in Bengali text documents that requires only a Part-Of-Speech (POS) tagger and a pre-trained language model trained on Bengali texts. |
| Outcome: | The proposed system outperforms baselines without human-annotated reference summaries on a human-random dataset with Bengali text. |
KidLM: Advancing Language Models for Children – Early Insights and Future Directions (2024.emnlp-main)
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| Challenge: | Large language models have been shown to be effective in creating educational tools for children, yet there are significant challenges in maintaining key child-specific properties such as linguistic nuances, cognitive needs, and safety standards. |
| Approach: | They propose a user-centric data collection pipeline that involves gathering and validating a corpus specifically written for and sometimes by children. |
| Outcome: | The proposed model excels in understanding lower grade-level text, maintains safety by avoiding stereotypes, and captures children’s unique preferences. |
XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags (2024.findings-acl)
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| Challenge: | XL-HeadTags is a dataset that includes 20 languages across 6 diverse language families. |
| Approach: | They propose to leverage auxiliary information such as images and captions embedded in news articles to retrieve relevant sentences and utilize instruction tuning with variations to generate both headlines and tags for news articles in a multilingual context. |
| Outcome: | The proposed approach generates headlines and tags in a multilingual context using images and captions embedded in the articles and instruction tuning with variations. |
Shironaam: Bengali News Headline Generation using Auxiliary Information (2023.eacl-main)
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| Challenge: | Automated headline generation systems have the potential to assist editors in finding interesting headlines to attract visitors or readers. |
| Approach: | They propose to use Bengali news article-headline pairings with auxiliary data to better model headline generation using pre-trained language models. |
| Outcome: | The proposed model improves on a Bengali news headline generation dataset by 3 to 10 percentage points over baselines. |
From Charts to Fair Narratives: Uncovering and Mitigating Geo-Economic Biases in Chart-to-Text (2025.emnlp-main)
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Ridwan Mahbub, Mohammed Saidul Islam, Mir Tafseer Nayeem, Md Tahmid Rahman Laskar, Mizanur Rahman, Shafiq Joty, Enamul Hoque
| Challenge: | Existing VLMs produce more positive descriptions for high-income countries compared to middle- or low-income nations, even when country attribution is the only variable changed. |
| Approach: | They propose to automate the process by generating textual summaries of charts using vision-language models to understand how a country’s economic status influences the sentiment of generated summary. |
| Outcome: | The proposed model amplifys geo-economic biases in 6,000 chart-country pairs from six widely used vision-language models to understand how a country’s economic status influences the sentiment of generated summaries. |
eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tables (2025.naacl-industry)
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| Challenge: | eC-Tab2Text dataset is designed to capture product attributes and user-specific queries. |
| Approach: | They propose a novel dataset to capture the intricacies of e-commerce including detailed product attributes and user-specific queries. |
| Outcome: | The proposed dataset outperforms existing generalpurpose LLMs in generating accurate product reviews. |
SurveyGen: Quality-Aware Scientific Survey Generation with Large Language Models (2025.emnlp-main)
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| Challenge: | Automated survey generation is a key task in scientific document processing due to lack of standardized evaluation datasets. |
| Approach: | They propose a survey-based framework that integrates quality indicators into literature retrieval to assess higher-quality sources. |
| Outcome: | The proposed framework enhances the standard Retrieval-Augmented Generation pipeline and enables human-guided writing. |
Deploying Tiny LVLM Judges for Real-World Evaluation of Chart Models: Lessons Learned and Best Practices (2025.emnlp-industry)
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Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Ridwan Mahbub, Mizanur Rahman, Amran Bhuiyan, Israt Jahan, Mir Tafseer Nayeem, Shafiq Joty, Enamul Hoque, Jimmy Huang
| Challenge: | Large Vision-Language Models (LVLMs) with only 7B parameters perform poorly as judges in resource-constrained settings. |
| Approach: | They propose two approaches to ensure costefficient evaluation by combining multiple criteria into a single query and domainadaptive transfer learning to create a 2Bparameter VLM on a chart dataset. |
| Outcome: | The proposed model can effectively transfer knowledge from one dataset to another to make it a more specialized model. |
Lost in Translation: Do LVLM Judges Generalize Across Languages? (2026.findings-acl)
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Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Mir Tafseer Nayeem, Amran Bhuiyan, Mizanur Rahman, Shafiq Joty, Enamul Hoque, Jimmy Huang
| Challenge: | MM-JudgeBench is the first large-scale benchmark for multilingual and multimodal judge model evaluation. |
| Approach: | They propose a multilingual benchmark for multilingual and multimodal judge model evaluation that includes over 60K pairwise preference instances spanning 25 typologically diverse languages. |
| Outcome: | The proposed benchmark includes over 60K pairwise preference instances spanning 25 languages. |
Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion (C18-1)
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| Challenge: | a new method for abstractive summarization is being developed for document summarizing . abstractive methods require extensive natural language generation to rewrite the sentences . |
| Approach: | They propose an unsupervised abstractive summarization system in multi-document context . they use a paraphrastic sentence fusion model which performs sentence synthesis and paraphrazing . |
| Outcome: | The proposed model improves information coverage and abstractiveness of generated sentences. |
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 . |
Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Models (2026.eacl-long)
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| Challenge: | Subword tokenization approaches misalign with linguistic structure and waste capacity across languages and domains. |
| Approach: | They argue for a context-aware framework that integrates tokenizer and model co-design . they argue that tokenization should be treated as a core design problem, not an afterthought . |
| Outcome: | The proposed framework integrates tokenizer and model co-design, guided by linguistic, domain, and deployment considerations. |