Summary of Beyond Captioning: Task-specific Prompting For Improved Vlm Performance in Mathematical Reasoning, by Ayush Singh et al.
Beyond Captioning: Task-Specific Prompting for Improved VLM Performance in Mathematical Reasoning
by Ayush Singh, Mansi Gupta, Shivank Garg, Abhinav Kumar, Vansh Agrawal
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 paper explores the limitations of Vision-Language Models (VLMs) in tasks requiring geometric reasoning, algebraic problem-solving, and counting. Despite their success in image retrieval and Visual Question Answering (VQA), VLMs struggle to effectively integrate multiple modalities and accurately interpret geometry-related tasks. The authors introduce a captioning pipeline before VQA tasks and find that it is not generalizable, especially for larger VLMs trained on downstream QnA tasks. Instead, they propose task-based prompting, enriching the prompt with task-specific guidance, which shows promise in math-heavy problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well computers can do certain tasks, like solving math problems or understanding pictures. Right now, these computers are pretty good at some things, but struggle with others. The authors want to make them better by giving them more information before they try to solve the problem. They found that this method doesn’t always work, especially when using really big and powerful computer programs. Instead, they suggest a new way of helping the computer understand what it needs to do, which seems to be working well for math-related tasks. |
Keywords
» Artificial intelligence » Prompt » Prompting » Question answering
