Summary of A Systematic Survey Of Prompt Engineering in Large Language Models: Techniques and Applications, by Pranab Sahoo et al.
A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
by Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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 presents a survey on prompt engineering, which is essential for extending the capabilities of large language models (LLMs) and vision-language models (VLMs). Prompt engineering leverages task-specific instructions to enhance model efficacy without modifying core parameters. This approach has been successful in various applications, from question-answering to commonsense reasoning. However, there is a lack of systematic organization and understanding of diverse prompt engineering methods. The paper provides a structured overview of recent advancements in prompt engineering, categorized by application area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Prompt engineering allows seamless integration of pre-trained models into downstream tasks by eliciting desired model behaviors solely based on given prompts. Prompts can be natural language instructions or learned vector representations that activate relevant knowledge. This has enabled success across various applications, from question-answering to commonsense reasoning. The paper also provides a taxonomy diagram and table summarizing datasets, models, and critical points of each prompting technique, enabling a better understanding of the rapidly developing field. |
Keywords
» Artificial intelligence » Prompt » Prompting » Question answering
