Summary of Mapo: Boosting Large Language Model Performance with Model-adaptive Prompt Optimization, by Yuyan Chen et al.
MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization
by Yuyan Chen, Zhihao Wen, Ge Fan, Zhengyu Chen, Wei Wu, Dayiheng Liu, Zhixu Li, Bang Liu, Yanghua Xiao
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 The paper proposes a novel approach to prompt engineering, which is crucial for leveraging Large Language Models (LLMs) efficiently and effectively. Unlike existing research, which focuses on adapting prompts to specific tasks rather than specific LLMs, the authors demonstrate that good prompts should be tailored to both the task and the nature of the LLM in question. They propose a model-adaptive prompt optimizer (MAPO) method that optimizes original prompts for each specific LLM in downstream natural language processing (NLP) tasks. Experimental results show that MAPO can refine prompts for an LLM, leading to significant improvements across various NLP tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to make the most out of large language models. It’s like trying to get a computer to do a specific job by giving it the right instructions. The authors found that these instructions, called “prompts,” need to be adjusted not only for what task you want the computer to do but also for which type of large language model you’re using. They came up with a new way to adjust prompts, called MAPO, and tested it on different tasks. It worked really well! |
Keywords
» Artificial intelligence » Large language model » Natural language processing » Nlp » Prompt
