Summary of Mope: Mixture Of Prompt Experts For Parameter-efficient and Scalable Multimodal Fusion, by Ruixiang Jiang et al.
MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal Fusion
by Ruixiang Jiang, Lingbo Liu, Changwen Chen
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces Mixture of Prompt Experts (MoPE), a novel technique that addresses the limitations of prompt-based multimodal fusion methods. MoPE decomposes standard prompts to capture instance-level features adaptively, enhancing expressiveness by leveraging multimodal pairing priors to route the most effective prompt for each instance dynamically. Compared to vanilla prompting, MoPE exhibits greater expressiveness, scaling effectively with training data and trainable parameters. The paper also investigates regularization terms for expert routing, leading to emergent expert specialization with enhanced adaptability and interpretability. Extensive experiments across six multimodal datasets demonstrate state-of-the-art performance for prompt fusion, matching or surpassing fine-tuning while requiring only 0.8% of trainable parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make computer models better at combining different types of information, like text and images. The current methods are good but limited, so the researchers created something called MoPE (Mixture of Prompt Experts) that can learn more from the data it’s trained on. This makes it more effective and efficient than before. They tested it with lots of different data sets and found that it works really well, even better than some other methods that require much more computer power. |
Keywords
* Artificial intelligence * Fine tuning * Prompt * Prompting * Regularization
