Summary of Electrovizqa: How Well Do Multi-modal Llms Perform in Electronics Visual Question Answering?, by Pragati Shuddhodhan Meshram et al.
ElectroVizQA: How well do Multi-modal LLMs perform in Electronics Visual Question Answering?
by Pragati Shuddhodhan Meshram, Swetha Karthikeyan, Bhavya, Suma Bhat
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed ElectroVizQA dataset aims to evaluate the performance of Multi-modal Large Language Models (MLLMs) on digital electronic circuit problems, a topic commonly found in undergraduate curricula. The dataset comprises approximately 626 visual questions, providing a comprehensive overview of digital electronics topics. MLLMs have demonstrated exceptional capabilities in tasks such as Visual Question Answering (VQA), but they often struggle with fundamental engineering problems. This paper rigorously assesses the extent to which MLLMs can understand and solve digital electronic circuit questions, offering insights into their capabilities and limitations within this specialized domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a special dataset called ElectroVizQA that helps computers learn to answer questions about how electronic circuits work. It has 626 questions that cover important topics in electronics classes. The goal is to see if these computer models can understand and solve problems like this, which will help them be better at solving real-world engineering challenges. |
Keywords
» Artificial intelligence » Multi modal » Question answering
