Papers by Enamul Hoque
Chart-to-Text: A Large-Scale Benchmark for Chart Summarization (2022.acl-long)
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Shankar Kantharaj, Rixie Tiffany Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, Shafiq Joty
| Challenge: | Inferring key insights from charts can be challenging and time-consuming. |
| Approach: | They propose a task where the goal is to explain a chart and summarize key takeaways from it in natural language. |
| Outcome: | The proposed model produces fluent summaries but suffers from hallucinations and factual errors . the proposed model is compared with other models and can be used to generate BLEU scores . |
Can Large Language Models Fix Data Annotation Errors? An Empirical Study Using Debatepedia for Query-Focused Text Summarization (2023.findings-emnlp)
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| Challenge: | Debatepedia dataset limited by noise and most queries do not have relevance to document . |
| Approach: | They harness the language generation capabilities of two LLMs to regenerate queries in a Debatepedia dataset. |
| Outcome: | The proposed model can regenerate queries from the Debatepedia dataset. |
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. |
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. |
UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning (2023.emnlp-main)
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| Challenge: | Existing methods for chart-based data analysis neglect explicit modeling of chart structures. |
| Approach: | They propose a pretrained model for chart comprehension and reasoning that encodes relevant text, data, and visual elements of charts and uses a chart-grounded text decoder for text generation. |
| Outcome: | The proposed model outperforms existing methods that lack explicit modeling of chart structures and lacks explicit modeling. |
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)
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Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang
| Challenge: | Large Language Models (LLMs) have gained significant attention due to their capabilities in performing diverse tasks across domains. |
| Approach: | They review the primary challenges and limitations causing inconsistencies in evaluations . early models could generate coherent text but limited to simple tasks . |
| Outcome: | The proposed evaluations are reproducible, reliable, and robust. |
DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts (2024.emnlp-main)
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| Challenge: | Data-driven storytelling uses visual aids and visualizations to convey insights. |
| Approach: | They propose a task for data story generation using large language models and a benchmark containing 1,449 stories from diverse sources. |
| Outcome: | The proposed framework outperforms non-agentic counterparts in both model-based and human evaluations, but also reveals unique challenges in data story generation. |
ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning (2022.findings-acl)
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| Challenge: | Existing datasets that focus on complex reasoning questions do not address such questions as they are template-based and answers come from a fixed-vocabulary. |
| Approach: | They propose a large-scale benchmark that uses visual and logical reasoning to answer questions using a transformer-based model. |
| Outcome: | The proposed models achieve state-of-the-art on the previous datasets and on the current one, but also show that they have several challenges in answering complex reasoning questions. |
NLP+Vis: NLP Meets Visualization (2023.emnlp-tutorial)
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| Challenge: | This tutorial will introduce NLP+Vis with a focus on two main threads of work: NLP for Vis and Vis for NLP. |
| Approach: | tutorial will introduce NLP+Vis with a focus on two main threads of work . overview of research topics on combining NLP and Vis techniques will be covered . |
| Outcome: | The tutorial will introduce NLP+Vis with a focus on two main threads of work . it will provide an overview of research topics on combining NLP and Vis techniques . |
Text2Vis: A Challenging and Diverse Benchmark for Generating Multimodal Visualizations from Text (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown promise in generating visualizations from natural language, but lack of comprehensive benchmarks limits their capabilities. |
| Approach: | They propose a framework that jointly refines the textual answer and visualization code to improve GPT-4o's pass rate from 26% to 42% over direct approach. |
| Outcome: | The proposed framework increases GPT-4o’s pass rate from 26% to 42% over the direct approach and improves chart quality. |
Open-RAG: Enhanced Retrieval Augmented Reasoning with Open-Source Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing methods to integrate Large Language Models with external knowledge suffer from limited reasoning capabilities, especially when using open-source LLMs. |
| Approach: | They propose a framework that transforms an arbitrary dense LLM into a parameter-efficient sparse mixture of experts (MoE) model capable of handling complex reasoning tasks. |
| Outcome: | The proposed framework transforms an arbitrary dense LLM into a parameter-efficient sparse mixture of experts (MoE) model capable of handling complex reasoning tasks, including both single- and multi-hop queries. |
ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval (2025.emnlp-industry)
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Ahmed Masry, Megh Thakkar, Patrice Bechard, Sathwik Tejaswi Madhusudhan, Rabiul Awal, Shambhavi Mishra, Akshay Kalkunte Suresh, Srivatsava Daruru, Enamul Hoque, Spandana Gella, Torsten Scholak, Sai Rajeswar
| Challenge: | Existing methods for multimodal document retrieval often replicate techniques developed for text-only retrieval. |
| Approach: | They propose a document retrieval model that bridges the gap between multimodal representation learning and document retrievals by providing external knowledge as context. |
| Outcome: | The proposed model achieves 3.61% improvement over existing retrieval models on the ViDoRe V2 benchmark, showing stronger generalization to out-of-domain benchmarks. |
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. |
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. |
WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization (2020.coling-main)
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| Challenge: | Existing methods to generate abstractive summarizations are lacking labeled training datasets. |
| Approach: | They propose a weakly supervised approach to generate a strong summary from a set of documents based on a query. |
| Outcome: | The proposed approach sets a new state-of-the-art in terms of evaluation metrics on the Document Understanding Conferences dataset. |
DashboardQA: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards (2026.findings-eacl)
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Aaryaman Kartha, Ahmed Masry, Mohammed Saidul Islam, Thinh Lang, Shadikur Rahman, Ridwan Mahbub, Mizanur Rahman, Mahir Ahmed, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty
| Challenge: | Existing question-answering benchmarks for data visualizations focus on static charts instead of interactive dashboards. |
| Approach: | They propose a benchmark to assess how vision-language GUI agents comprehend and interact with real-world dashboards. |
| Outcome: | The first benchmark explicitly designed to assess how vision-language GUI agents comprehend and interact with real-world dashboards. |
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. |
Improving Automatic Evaluation of Large Language Models (LLMs) in Biomedical Relation Extraction via LLMs-as-the-Judge (2025.acl-long)
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| Challenge: | Large Language Models generate human-like text, making them unreliable for biomedical relation extraction tasks. |
| Approach: | They propose to use Large Language Models as judges to evaluate biomedical relation extraction . they propose structured output formatting for LLM-generated responses that helps LLMs improve their performance by 15%. |
| Outcome: | The proposed method improves LLM-Judges' performance by 15% . it is cheaper and more efficient than human evaluation metrics, the authors say . |
OpenCQA: Open-ended Question Answering with Charts (2022.emnlp-main)
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| Challenge: | OpenCQA is a task to answer open-ended questions about charts with descriptive texts. |
| Approach: | They propose a task to answer open-ended questions about charts with descriptive texts. |
| Outcome: | The proposed task is to answer an open-ended question about a chart with descriptive texts. |
Aligning Text, Code, and Vision: A Multi-Objective Reinforcement Learning Framework for Text-to-Visualization (2026.eacl-long)
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| Challenge: | Text2Vis systems generate functional code but resulting charts lack semantic alignment and clarity. |
| Approach: | They propose a framework that integrates post-execution feedback with textual accuracy, code validity, and visualization quality. |
| Outcome: | The proposed framework outperforms strong zero-shot and supervised baselines and shows robust generalization to out-of-domain datasets. |
ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning (2024.findings-acl)
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| Challenge: | Charts provide visual representations of data and are used for analyzing information, addressing queries, and conveying insights to others. |
| Approach: | They propose a chart-specific vision-language Instruction-following dataset with 191K instructions and a pipeline model that extracts chart data tables and inputs them into a LLM. |
| Outcome: | The proposed model can solve a wide range of chart-related tasks, achieving state-of-the-art results on four tasks. |
Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task (2020.lrec-1)
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| Challenge: | Word embeddings that consider context have attracted great attention for natural language processing tasks in recent years. |
| Approach: | They propose two different approaches to integrate contextualized word embeddings with transformer encoders for sentence similarity modeling. |
| Outcome: | The proposed model outperforms the feature-based approach on six datasets. |
ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering (2025.findings-acl)
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Ahmed Masry, Mohammed Saidul Islam, Mahir Ahmed, Aayush Bajaj, Firoz Kabir, Aaryaman Kartha, Md Tahmid Rahman Laskar, Mizanur Rahman, Shadikur Rahman, Mehrad Shahmohammadi, Megh Thakkar, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty
| Challenge: | Chart Question Answering systems are limited in their ability to interpret data visually and reason with visual representations. |
| Approach: | They propose a chart-based chart question-answering system that includes 1,341 charts from 99 diverse sources and 1,948 questions in various types. |
| Outcome: | The new benchmark includes 1,341 charts from 99 diverse sources and 1,948 questions in various types. |
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 . |
ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild (2025.coling-industry)
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| Challenge: | Existing methods for chart understanding and reasoning are weakly aligned and rely on underlying data tables. |
| Approach: | They propose a chart-based understanding and reasoning model that is trained on instruction-tuning data generated directly from chart images. |
| Outcome: | The proposed model achieves state-of-the-art results across 5 benchmarks spanning chart summarization, question answering, and fact-checking. |
Multimodal Large Language Models for Human-AI Interaction: Foundations, Agents, and Inclusive Applications (2026.eacl-tutorials)
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| Challenge: | This tutorial presents foundations, agentic capabilities, and inclusive applications of multimodal large language models. |
| Approach: | This tutorial presents foundations, agentic capabilities, and inclusive applications of multimodal large language models. |
| Outcome: | This tutorial covers foundations, agentic capabilities, and inclusive applications of multimodal large language models. |