Summary of Prompt Optimization with Ease? Efficient Ordering-aware Automated Selection Of Exemplars, by Zhaoxuan Wu et al.
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars
by Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low
First submitted to arxiv on: 25 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Machine Learning (stat.ML)
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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 proposed EASE method leverages a pre-trained language model’s hidden embedding to represent ordered sets of exemplars and optimizes them using a neural bandit algorithm. This approach eliminates test-time computation by finding an optimal set of exemplars that performs well for all test queries from a given task. The method can be extended to jointly optimize both the exemplars and the instruction, offering practical insights into the impact of exemplar selection on in-context learning. EASE outperforms existing methods through extensive empirical evaluations, including novel tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research paper, scientists developed a new way to help large language models learn from examples without needing extra training. They called it EASE, and it uses special representations of ordered sets of examples to make the model more accurate. This method doesn’t require any additional computation during testing and can be used with different tasks. The researchers tested their approach and found that it worked better than previous methods. This has important implications for how we use language models in real-world applications. |
Keywords
» Artificial intelligence » Embedding » Language model
