Summary of Prewrite: Prompt Rewriting with Reinforcement Learning, by Weize Kong and Spurthi Amba Hombaiah and Mingyang Zhang and Qiaozhu Mei and Michael Bendersky
PRewrite: Prompt Rewriting with Reinforcement Learning
by Weize Kong, Spurthi Amba Hombaiah, Mingyang Zhang, Qiaozhu Mei, Michael Bendersky
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research paper proposes a novel approach to prompt engineering for large language models (LLMs), aiming to improve the efficiency and effectiveness of LLM-based applications. The authors acknowledge that current manual methods are time-consuming, ineffective, and sub-optimal, leading to a pressing need for automated solutions. They explore ways to refine prompts, enabling further enhancements and optimizations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is trying to make it easier and better to work with big language models. Right now, people have to do this process manually, which can take a lot of time and effort. The researchers want to find a way to automate this process so that it’s faster, more efficient, and produces better results. |
Keywords
* Artificial intelligence * Prompt
