Summary of Ipo: Interpretable Prompt Optimization For Vision-language Models, by Yingjun Du et al.
IPO: Interpretable Prompt Optimization for Vision-Language Models
by Yingjun Du, Wenfang Sun, Cees G. M. Snoek
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); 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 This paper proposes a novel approach to optimizing prompts for pre-trained vision-language models like CLIP. The existing methods rely on gradient descent to learn prompts, which can lead to overfitting and prompts that are no longer understandable by humans. Instead, the authors introduce an Interpretable Prompt Optimizer (IPO) that utilizes large language models (LLMs) to generate textual prompts dynamically. The IPO is conditioned on visual content through a large multimodal model (LMM), allowing for the creation of dataset-specific prompts that improve generalization performance while maintaining human comprehension. Experimental results across 11 datasets demonstrate that IPO not only improves accuracy but also enhances interpretability, ensuring that prompts remain human-understandable and facilitating better transparency and oversight for vision-language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computer programs better understand images and text by improving the way they are asked questions. Right now, these programs are good at understanding what’s in a picture or what words mean, but they need help to figure out how to ask good questions about it. The authors of this paper created a new way to do this called the Interpretable Prompt Optimizer (IPO). It uses really big computers that can understand lots of language to come up with good questions that humans would also understand. This makes it easier for people to use these programs and make sure they are doing what we want them to do. |
Keywords
» Artificial intelligence » Generalization » Gradient descent » Overfitting » Prompt
