2 citations · 9 across the 23 of their papers we have counts for
23 papers
Training Therapeutic Judges and Multi-Agent Systems for Human-Aligned Mental Health Support
Mizanur Rahman, Abeer Badawi, Elahe Rahimi +4
Large language models show promise for mental health support, yet therapeutic quality improves only when evaluation functions as an actionable control signal rather than a passive…
Chart Deception in Vision-Language Models: From Vulnerability to Mitigation
Ridwan Mahbub, Mohammed Saidul Islam, Md Tahmid Rahman Laskar +3
Information visualizations are widely used to communicate patterns, trends, and outliers, yet deceptive design choices-such as truncated or inverted axes, distorted aspect ratios,…
DATAREEL: Automated Data-Driven Video Story Generation with Animations
Ridwan Mahbub, Syem Aziz, Mizanur Rahman +4
Data videos combine animated visualizations with synchronized narration to communicate quantitative information and are widely used in journalism, education, and public communicati…
Aligning Text, Code, and Vision: A Multi-Objective Reinforcement Learning Framework for Text-to-Visualization
Mizanur Rahman, Mohammed Saidul Islam, Md Tahmid Rahman Laskar +2
Text-to-Visualization (Text2Vis) systems translate natural language queries over tabular data into concise answers and executable visualizations. While closed-source LLMs generate…
ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval
Ahmed Masry, Megh Thakkar, Patrice Bechard +9
Retrieval-augmented generation has proven practical when models require specialized knowledge or access to the latest data. However, existing methods for multimodal document retrie…
Deploying Tiny LVLM Judges for Real-World Evaluation of Chart Models: Lessons Learned and Best Practices
Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Ridwan Mahbub +7
Large Vision-Language Models (LVLMs) with only 7B parameters have shown promise as automated judges in chart comprehension tasks. However, tiny models (<=2B parameters) still perfo…
LLM-Based Data Science Agents: A Survey of Capabilities, Challenges, and Future Directions
Mizanur Rahman, Amran Bhuiyan, Mohammed Saidul Islam +5
Recent advances in large language models (LLMs) have enabled a new class of AI agents that automate multiple stages of the data science workflow by integrating planning, tool use,…
DashboardQA: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards
Aaryaman Kartha, Ahmed Masry, Mohammed Saidul Islam +8
Dashboards are powerful visualization tools for data-driven decision-making, integrating multiple interactive views that allow users to explore, filter, and navigate data. Unlike s…
BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning
Ahmed Masry, Abhay Puri, Masoud Hashemi +13
Charts are essential to data analysis, transforming raw data into clear visual representations that support human decision-making. Although current vision-language models (VLMs) ha…
The Perils of Chart Deception: How Misleading Visualizations Affect Vision-Language Models
Ridwan Mahbub, Mohammed Saidul Islam, Md Tahmid Rahman Laskar +3
Information visualizations are powerful tools that help users quickly identify patterns, trends, and outliers, facilitating informed decision-making. However, when visualizations i…
From Charts to Fair Narratives: Uncovering and Mitigating Geo-Economic Biases in Chart-to-Text
Ridwan Mahbub, Mohammed Saidul Islam, Mir Tafseer Nayeem +4
Charts are very common for exploring data and communicating insights, but extracting key takeaways from charts and articulating them in natural language can be challenging. The cha…
Text2Vis: A Challenging and Diverse Benchmark for Generating Multimodal Visualizations from Text
Mizanur Rahman, Md Tahmid Rahman Laskar, Shafiq Joty +1
Automated data visualization plays a crucial role in simplifying data interpretation, enhancing decision-making, and improving efficiency. While large language models (LLMs) have s…
Improving Automatic Evaluation of Large Language Models (LLMs) in Biomedical Relation Extraction via LLMs-as-the-Judge
Md Tahmid Rahman Laskar, Israt Jahan, Elham Dolatabadi +3
Large Language Models (LLMs) have demonstrated impressive performance in biomedical relation extraction, even in zero-shot scenarios. However, evaluating LLMs in this task remains…
Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning?
Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Ridwan Mahbub +7
Charts are ubiquitous as they help people understand and reason with data. Recently, various downstream tasks, such as chart question answering, chart2text, and fact-checking, have…
ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering
Ahmed Masry, Mohammed Saidul Islam, Mahir Ahmed +11
Charts are ubiquitous, as people often use them to analyze data, answer questions, and discover critical insights. However, performing complex analytical tasks with charts requires…
AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understanding
Ahmed Masry, Juan A. Rodriguez, Tianyu Zhang +19
Aligning visual features with language embeddings is a key challenge in vision-language models (VLMs). The performance of such models hinges on having a good connector that maps vi…
Open-RAG: Enhanced Retrieval-Augmented Reasoning with Open-Source Large Language Models
Shayekh Bin Islam, Md Asib Rahman, K S M Tozammel Hossain +3
Retrieval-Augmented Generation (RAG) has been shown to enhance the factual accuracy of Large Language Models (LLMs), but existing methods often suffer from limited reasoning capabi…
DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts
Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Md Rizwan Parvez +2
Data-driven storytelling is a powerful method for conveying insights by combining narrative techniques with visualizations and text. These stories integrate visual aids, such as hi…
ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild
Ahmed Masry, Megh Thakkar, Aayush Bajaj +3
Given the ubiquity of charts as a data analysis, visualization, and decision-making tool across industries and sciences, there has been a growing interest in developing pre-trained…
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations
Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari +10
Large Language Models (LLMs) have recently gained significant attention due to their remarkable capabilities in performing diverse tasks across various domains. However, a thorough…
Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning? An Extensive Investigation into the Capabilities and Limitations of LVLMs
Mohammed Saidul Islam, Raian Rahman, Ahmed Masry +3
Natural language is a powerful complementary modality of communication for data visualizations, such as bar and line charts. To facilitate chart-based reasoning using natural langu…
ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning
Ahmed Masry, Mehrad Shahmohammadi, Md Rizwan Parvez +2
Charts provide visual representations of data and are widely used for analyzing information, addressing queries, and conveying insights to others. Various chart-related downstream…
Do LLMs Work on Charts? Designing Few-Shot Prompts for Chart Question Answering and Summarization
Xuan Long Do, Mohammad Hassanpour, Ahmed Masry +3
A number of tasks have been proposed recently to facilitate easy access to charts such as chart QA and summarization. The dominant paradigm to solve these tasks has been to fine-tu…