Papers with chart reasoning tasks
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. |
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. |
ChartGaze: Enhancing Chart Understanding in LVLMs with Eye-Tracking Guided Attention Refinement (2025.emnlp-main)
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Ali Salamatian, Amirhossein Abaskohi, Wan-Cyuan Fan, Mir Rayat Imtiaz Hossain, Leonid Sigal, Giuseppe Carenini
| Challenge: | Chart question answering (CQA) is a key research challenge for large vision-language models . recent efforts focus on leveraging LVLMs directly on chart images . |
| Approach: | They propose a gaze-guided attention refinement that aligns image-text attention with human fixations to improve chart reasoning quality and interpretability. |
| Outcome: | The proposed approach improves answer accuracy and attention alignment yielding gains of up to 2.56 percentage points across multiple models. |