# Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

**Authors:** Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, Xia Hu  
**arXiv:** [2304.13712](https://arxiv.org/abs/2304.13712)  
**Date:** 2023-04-26  
**Summary page:** https://www.summarizepaper.com/en/paper/2304.13712v2/

## Key points

- The paper is a comprehensive and practical guide for practitioners and end-users who work with Large Language Models (LLMs) in their downstream natural language processing (NLP) tasks.
- The authors provide discussions and insights into the usage of LLMs from the perspectives of models, data, and downstream tasks.
- The paper offers an introduction to current GPT- and BERT-style LLMs, followed by a detailed discussion about the use cases and non-use cases of large language models for various natural language processing tasks such as knowledge-intensive tasks, traditional natural language understanding tasks, natural language generation tasks as well as emergent abilities.
- It explores considerations for specific tasks such as efficiency cost latency to ensure successful implementation in a wide range of NLP tasks.
- The authors present various use cases and non-use cases to illustrate the practical applications and limitations of LLMs in real-world scenarios.
- They also try to understand the importance of data and the specific challenges associated with each NLP task.
- Furthermore, they explore the impact of spurious biases on LLMs which can be detrimental to successful implementation in practice.
- This guide includes a curated list of practical guide resources regularly updated which can be found at \url{https://github.com/Mooler0410/LLMsPracticalGuide}.

## Summary

This paper, titled "Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond," is a comprehensive and practical guide for practitioners and end-users who work with Large Language Models (LLMs) in their downstream natural language processing (NLP) tasks. The authors, Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin and Xia Hu provide discussions and insights into the usage of LLMs from the perspectives of models, data and downstream tasks. The paper begins by offering an introduction to current GPT- and BERT-style LLMs. Then it delves into the influence of pre-training data, training data and test data. Most importantly it provides a detailed discussion about the use cases and non-use cases of large language models for various natural language processing tasks such as knowledge-intensive tasks, traditional natural language understanding tasks, natural language generation tasks as well as emergent abilities. It also explores considerations for specific tasks such as efficiency cost latency to ensure successful implementation in a wide range of NLP tasks. The authors present various use cases and non-use cases to illustrate the practical applications and limitations of LLMs in real world scenarios. They also try to understand the importance of data and the specific challenges associated with each NLP task. Furthermore they explore the impact of spurious biases on LLMs which can be detrimental to successful implementation in practice. In addition to providing valuable insights into working with LLMs for researchers and practitioners alike this guide also includes a curated list of practical guide resources regularly updated which can be found at \url{https://github.com/Mooler0410/LLMsPracticalGuide}. Overall this paper serves as an excellent resource for anyone looking to harness the power of Large Language Models in their NLP work. The authors' insights and best practices will undoubtedly prove invaluable to those looking to implement LLMs in real world scenarios.