Papers by Adnan Qidwai
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. |