Summary of Chain-of-thought Prompting For Demographic Inference with Large Multimodal Models, by Yongsheng Yu et al.
Chain-of-Thought Prompting for Demographic Inference with Large Multimodal Models
by Yongsheng Yu, Jiebo Luo
First submitted to arxiv on: 24 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 paper explores the application of large multimodal models (LMMs) to demographic inference, introducing a benchmark for both quantitative and qualitative evaluation. LMMs are found to possess advantages in zero-shot learning, interpretability, and handling uncurated ‘in-the-wild’ inputs, albeit with a propensity for off-target predictions. To mitigate this issue, the authors propose a Chain-of-Thought augmented prompting approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses large multimodal models (LMMs) to help make better guesses about people’s demographics without needing labeled data. They found that LMMs can do some things well, like learning new tasks and understanding what people mean when they talk or write. However, they also have some problems, like making mistakes and not being able to explain why they made those mistakes. To fix this, the researchers came up with a new way of asking the models questions that helps them be more accurate. |
Keywords
» Artificial intelligence » Inference » Prompting » Zero shot
