Key Points

1. Large language models (LLMs) have achieved remarkable performance on a wide array of NLP tasks, which has attracted significant interest from academia and various industries.

2. Prompt engineering is the process of creating natural language instructions (prompts) to extract knowledge from LLMs in an organized manner, without requiring extensive parameter re-training or fine-tuning.

3. This paper enumerates several prompting strategies and groups them according to different NLP tasks, providing a taxonomy diagram, tabulating the techniques tried on various datasets, discussing the LLMs employed, and listing potential state-of-the-art (SoTA) methods for each dataset.

4. The authors have reviewed and analyzed 44 research papers, the majority of which have been published in the last two years, covering 39 prompting techniques applied on 29 different NLP tasks.

5. The paper discusses various prompting techniques such as basic prompting, chain-of-thought, self-consistency, program-aided language models, and techniques to address challenges like hallucination and reasoning in chaotic contexts.

6. The performance of prompting techniques varies across different NLP tasks and datasets, with some techniques outperforming others by significant margins.

7. The choice of LLM also plays a crucial role in the effectiveness of prompting techniques, with newer and larger models generally performing better.

8. The paper highlights the lack of prior systematic surveys on prompt engineering and notes that this work takes a more granular categorization of prompting strategies based on NLP tasks compared to previous broad surveys.

9. The authors conclude by emphasizing the importance of prompt engineering in realizing the full potential of LLMs and the need for further research in this rapidly evolving field.

Summary

Utilizing Large Language Models: Large language models (LLMs) have achieved unprecedented performance on a wide range of NLP tasks due to their ability to learn from vast amounts of text data. However, utilizing the full potential of these LLMs often requires extensive retraining or fine-tuning, which can be time-consuming and resource-intensive. Prompt engineering has emerged as a powerful approach to leverage the capabilities of LLMs without the need for extensive retraining.

Large language models (LLMs) have achieved unprecedented performance on a wide range of NLP tasks due to their ability to learn from vast amounts of text data. However, utilizing the full potential of these LLMs often requires extensive retraining or fine-tuning, which can be time-consuming and resource-intensive. Prompt engineering has emerged as a powerful approach to leverage the capabilities of LLMs without the need for extensive retraining.

Prompt Engineering Methodology: Prompt engineering involves composing natural language instructions, called prompts, to elicit knowledge from LLMs in a structured way. Unlike previous state-of-the-art models, prompt engineering solely operates on the embedded knowledge of LLMs, making LLMs more accessible to a wider audience, even those without a deep machine learning background.

Prompt engineering involves composing natural language instructions, called prompts, to elicit knowledge from LLMs in a structured way. Unlike previous state-of-the-art models, prompt engineering solely operates on the embedded knowledge of LLMs, making LLMs more accessible to a wider audience, even those without a deep machine learning background.

Summary and Analysis of Research Papers: The paper reviews 44 research papers that discuss 39 different prompting techniques applied to 29 different NLP tasks, the majority of which have been published in the last two years. It presents a taxonomy diagram that organizes the various prompting strategies based on the NLP tasks they have been used for. For each NLP task, the paper provides a detailed analysis of the prompting techniques that have been explored, their performance on various datasets, and the LLMs used. The paper also identifies the potential state-of-the-art prompting method for each dataset.

The paper reviews 44 research papers that discuss 39 different prompting techniques applied to 29 different NLP tasks, the majority of which have been published in the last two years. It presents a taxonomy diagram that organizes the various prompting strategies based on the NLP tasks they have been used for. For each NLP task, the paper provides a detailed analysis of the prompting techniques that have been explored, their performance on various datasets, and the LLMs used. The paper also identifies the potential state-of-the-art prompting method for each dataset.

Significance of Prompt Engineering: The survey highlights the importance of prompt engineering in extracting knowledge from LLMs and achieving significant performance gains on various NLP tasks. It demonstrates how prompt engineering can make LLMs more accessible and efficient by avoiding the need for extensive retraining or fine-tuning. The paper's comprehensive coverage of different prompting techniques and their applications provides a valuable resource for researchers and practitioners interested in leveraging the capabilities of LLMs for their NLP tasks.

The survey highlights the importance of prompt engineering in extracting knowledge from LLMs and achieving significant performance gains on various NLP tasks. It demonstrates how prompt engineering can make LLMs more accessible and efficient by avoiding the need for extensive retraining or fine-tuning. The paper's comprehensive coverage of different prompting techniques and their applications provides a valuable resource for researchers and practitioners interested in leveraging the capabilities of LLMs for their NLP tasks.

Reference: https://arxiv.org/abs/2407.12994