Large language models (LLMs) have revolutionized natural language processing (NLP) by emphasizing the importance of increasing parameters and training data for various reasoning tasks. However, LLMs struggle with tasks involving visual and spatial reasoning. To address this, researchers from Columbia University have introduced the Whiteboard-of-Thought (WoT) prompting method for multimodal large language models (MLLMs). WoT allows MLLMs to draw reasoning steps as images on a metaphorical ‘whiteboard’ and then process these images for better understanding and problem-solving. This approach has shown significant improvements in tasks requiring visual and spatial reasoning compared to traditional text-based reasoning methods.
The WoT method aims to enhance MLLMs’ visual reasoning abilities by enabling them to create and process images to answer queries effectively. The research highlights the limitations of current LLMs in handling tasks beyond 2D grid settings and emphasizes the need for accurate vision systems in future developments. WoT has demonstrated state-of-the-art results in challenging natural language tasks that demand visual and spatial understanding, showcasing its potential to bridge the gap between text-based reasoning and visual processing in MLLMs.
Overall, WoT presents a zero-shot method for enhancing visual reasoning across modalities in MLLMs, providing a promising avenue for future research to improve the capabilities of state-of-the-art models in understanding detailed geometric figures. The research paper and project details can be found in the provided links, with credit given to the researchers involved in the project.
Source link
Source link: https://www.marktechpost.com/2024/06/24/whiteboard-of-thought-wot-prompting-a-simple-ai-approach-to-enhance-the-visual-reasoning-abilities-of-mllms-across-modalities/?amp