Summary of by My Eyes: Grounding Multimodal Large Language Models with Sensor Data Via Visual Prompting, By Hyungjun Yoon et al.
By My Eyes: Grounding Multimodal Large Language Models with Sensor Data via Visual Prompting
by Hyungjun Yoon, Biniyam Aschalew Tolera, Taesik Gong, Kimin Lee, Sung-Ju Lee
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 This research proposes a novel approach to utilizing large language models (LLMs) for ubiquitous sensing applications. The existing text-prompt methods show significant performance degradation when handling long sensor data sequences, making it challenging to leverage LLMs for these tasks. To address this issue, the authors introduce a visual prompting method that uses multimodal LLMs (MLLMs) and designs a visual prompt that directs MLLMs to utilize visualized sensor data alongside target sensory task descriptions. The proposed approach also includes a visualization generator that automates the creation of optimal visualizations tailored to a given sensory task, eliminating the need for prior task-specific knowledge. The authors evaluate their method on nine sensory tasks involving four sensing modalities and achieve an average of 10% higher accuracy than text-based prompts while reducing token costs by 15.8 times. The findings highlight the effectiveness and cost-efficiency of visual prompts with MLLMs for various sensory tasks, opening up new possibilities for leveraging LLMs in ubiquitous sensing applications. The source code is available at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research shows how large language models can be used to help machines understand different types of data from sensors. Right now, these models don’t work well when dealing with long sequences of sensor data. To fix this, the authors came up with a new way to give the models information using visual prompts instead of text prompts. This helps the models understand what the sensor data is about and makes them more accurate. The authors also created a tool that automatically generates the best visual prompts for each task, so you don’t need special knowledge to use it. They tested this approach on many different sensing tasks and found that it worked better than using text prompts. This could be an important step towards making machines smarter and able to understand more types of data. |
Keywords
» Artificial intelligence » Prompt » Prompting » Token
