Papers by Srija Mukhopadhyay
MAPWise: Evaluating Vision-Language Models for Advanced Map Queries (2025.naacl-long)
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| Challenge: | Vision-language models excel at tasks requiring joint understanding of visual information and natural language. |
| Approach: | They propose to use choropleth maps to answer questions from three geographical regions in the United States, India, China as question templates. |
| Outcome: | The proposed model outperforms other models in the area of visual language and visual question answering. |
Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness (2024.findings-emnlp)
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| Challenge: | Chart question answering (CQA) is a crucial area of Visual Language Understanding. |
| Approach: | They evaluate the robustness and consistency of current Visual Language Models on a dataset encompassing diverse question categories and chart formats. |
| Outcome: | The proposed models handle varying levels of chart and question complexity and are robust across different visual representations of the same underlying data. |
PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights (2025.acl-demo)
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| Challenge: | Accurate and complete product descriptions are laborious to sift through manually. |
| Approach: | They propose a system that uses Large Language Models to extract, compare, and structure insights from customer reviews and seller descriptions. |
| Outcome: | The proposed system can extract, compare, and structure insights from customer reviews and seller descriptions. |