Summary of Synthesize, Partition, Then Adapt: Eliciting Diverse Samples From Foundation Models, by Yeming Wen et al.
Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models
by Yeming Wen, Swarat Chaudhuri
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 framework called Synthesize-Partition-Adapt (SPA) to generate diverse responses from foundation models while maintaining accuracy. The SPA framework leverages synthetic data to elicit unique aspects of the data and trains multiple model adaptations optimized for specific subsets. This approach is shown to be effective in diversifying responses in various tasks, including code generation and natural language understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models can provide users with diverse responses by generating multiple high-quality and unique answers. However, this often requires sacrificing accuracy or using greedy sampling methods that are limited. The new SPA framework uses synthetic data to help foundation models generate more varied responses while still being accurate. This approach is tested on different tasks, like code generation and understanding language, and shows promise in enriching user experience across various applications. |
Keywords
» Artificial intelligence » Language understanding » Synthetic data
