Summary of Teach Better or Show Smarter? on Instructions and Exemplars in Automatic Prompt Optimization, by Xingchen Wan et al.
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization
by Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Sercan O. Arik
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 investigates the performance of automatic prompt optimization (APO) methods for large language models. Specifically, it compares representative instruction optimization (IO) and exemplar optimization (EO) techniques on various challenging tasks. The study reveals that intelligently reusing model-generated input-output pairs as exemplars can improve performance on top of IO methods, but this approach is under-investigated. Additionally, the paper finds that EO strategies, even simple random search, outperform state-of-the-art IO methods without optimization. Furthermore, it observes a synergy between EO and IO, with optimal combinations surpassing individual contributions. The study concludes that studying exemplar optimization as a standalone method and its optimal combination with instruction optimization is crucial for APO and deserves greater consideration in future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are very good at understanding human language, but they need help to perform well on specific tasks. One way to give them this help is by creating special prompts that tell the model what to do. Researchers have developed different methods to optimize these prompts, which can be divided into two main categories: instruction optimization (IO) and exemplar optimization (EO). This study compares these two approaches and finds that a simple method called random search can actually perform better than more complex IO methods if used correctly. The study also shows that combining IO and EO methods can lead to even better results. Overall, this research highlights the importance of studying how to optimize prompts for large language models. |
Keywords
* Artificial intelligence * Optimization * Prompt
