Summary of Whiteboard-of-thought: Thinking Step-by-step Across Modalities, by Sachit Menon and Richard Zemel and Carl Vondrick
Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities
by Sachit Menon, Richard Zemel, Carl Vondrick
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 A novel approach is introduced to enhance the visual reasoning capabilities of multimodal large language models. The method, called whiteboard-of-thought prompting, provides a metaphorical “whiteboard” for the model to draw out reasoning steps as images, which are then returned for further processing. This technique leverages the model’s existing ability to write code with libraries like Matplotlib and Turtle, without requiring demonstrations or specialized modules. The approach is tested on four natural language tasks that involve visual and spatial reasoning, showing state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a computer trying to solve math problems or understand maps. Right now, it’s not very good at this kind of thinking. But what if we could teach the computer to draw pictures to help it figure things out? That’s basically what this research paper is all about. The authors came up with a new way to make computers better at understanding visual information, like maps or diagrams. They tested their idea on some tricky math problems and found that it worked really well! |
Keywords
» Artificial intelligence » Prompting
