Summary of An Empirical Categorization Of Prompting Techniques For Large Language Models: a Practitioner’s Guide, by Oluwole Fagbohun et al.
An Empirical Categorization of Prompting Techniques for Large Language Models: A Practitioner’s Guide
by Oluwole Fagbohun, Rachel M. Harrison, Anton Dereventsov
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); 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 This paper tackles the challenge of harnessing Large Language Models (LLMs) through prompt engineering. Despite the rapid growth in LLM development, the plethora of available prompt techniques creates a daunting landscape for practitioners. To streamline this process, the authors propose a comprehensive categorization framework to standardize and classify prompting methods into seven distinct categories. By examining well-known techniques from both academic and practical perspectives, the paper aims to provide a structured approach to understanding and applying these prompts in various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to use special computers called Large Language Models (LLMs) by giving them instructions, or “prompts”. There are many different ways to do this, but it can be hard to know which ones work best. The authors of this survey tried to fix that problem by grouping all the known methods into seven categories and explaining what each one does. They also showed some real-life examples to help us understand how we can use these prompts in our own projects. By making it easier for people to find and use the right prompts, this paper helps us get more out of LLMs. |
Keywords
* Artificial intelligence * Prompt * Prompting
