Summary of Exploring Prompt Engineering: a Systematic Review with Swot Analysis, by Aditi Singh et al.
Exploring Prompt Engineering: A Systematic Review with SWOT Analysis
by Aditi Singh, Abul Ehtesham, Gaurav Kumar Gupta, Nikhil Kumar Chatta, Saket Kumar, Tala Talaei Khoei
First submitted to arxiv on: 9 Oct 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 A comprehensive SWOT analysis is conducted on various prompt engineering techniques within Large Language Models (LLMs), focusing on linguistic principles to identify strengths, weaknesses, opportunities, and threats. This study aims to enhance AI interactions and improve language model comprehension of human prompts by examining template-based approaches and fine-tuning. The findings provide insights into the problems and challenges associated with each technique, offering future research directions for optimizing human-machine communication. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make Large Language Models (LLMs) understand human language better. They do this by analyzing different ways to create prompts that LLMs can use. The researchers find what works well and what doesn’t, and they discuss the problems and challenges with each approach. Their study aims to help improve communication between humans and machines. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Prompt
