Summary of An Online Learning Approach to Prompt-based Selection Of Generative Models, by Xiaoyan Hu et al.
An Online Learning Approach to Prompt-based Selection of Generative Models
by Xiaoyan Hu, Ho-fung Leung, Farzan Farnia
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 a novel approach to selecting the best text-based generative model for a given input prompt, rather than relying on averaged evaluation scores. The authors propose an online learning framework called PAK-UCB that predicts the best data generation model for unseen prompts by leveraging random Fourier features (RFF) and kernel-based functions. This approach addresses a contextual bandit setting with shared context variables across arms, allowing for efficient identification of the optimal model. Experimental results on real and simulated text-to-image and image-to-text generative models demonstrate the effectiveness of RFF-UCB in identifying the best generation model across different sample types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have many machines that can generate pictures or text based on what you tell them to do. Most people pick the one that does the best job overall, but this approach doesn’t take into account that each machine might be better at doing certain things. The paper proposes a new way to choose which machine to use for a specific task by learning from the data they generate. This helps reduce waste and makes the process more efficient. |
Keywords
» Artificial intelligence » Generative model » Online learning » Prompt
