Summary of A Survey Of Prompt Engineering Methods in Large Language Models For Different Nlp Tasks, by Shubham Vatsal and Harsh Dubey
A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks
by Shubham Vatsal, Harsh Dubey
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Large language models (LLMs) have achieved impressive results across various Natural Language Processing (NLP) tasks, and prompt engineering plays a crucial role in enhancing their performance. By crafting natural language instructions, or prompts, researchers can elicit knowledge from LLMs in a structured manner, without requiring extensive re-training or fine-tuning of the models. This approach allows LLM enthusiasts to extract valuable insights through simple conversational exchanges or prompting, making it accessible to those without advanced machine learning backgrounds. As prompt engineering has gained popularity over the past two years, researchers have developed various techniques for designing prompts that improve information extraction from LLMs. This paper provides a comprehensive overview of different prompting strategies, grouped by NLP task, and evaluates their performance on specific datasets using corresponding LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can do many things well, like understand human languages. To help them do even more, researchers use “prompts” to ask the right questions. This makes it easier for people without advanced math or computer science backgrounds to work with these powerful models. The researchers have come up with many ways to design prompts that get better results from the language models. In this paper, they share all their ideas and show which ones work best on different tasks. |
Keywords
» Artificial intelligence » Fine tuning » Machine learning » Natural language processing » Nlp » Prompt » Prompting
