# 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/2304.13712v1/

## Key points

- Comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in NLP tasks
- Discussions and insights on LLM usage from various perspectives: models, data, pre-training data, training data, and test data as well as downstream tasks
- Introduction and summary of GPT- and BERT-style LLMs
- Influence of pre-training data, training data, and test data on LLM performance
- Understanding use cases and non-use cases of LLMs for different NLP tasks
- Practical applications and limitations of LLMs in real-world scenarios
- Significance of data quality and challenges associated with each NLP task
- Impact of spurious biases on LLMs
- Considerations for efficiency, cost, latency in deploying LLMs
- Insights and best practices for researchers and practitioners working with LLMs
- Curated list of regularly updated practical guide resources for LLMs

## 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 provide valuable discussions and insights into the usage of LLMs from various perspectives, including models, data, pre-training data, training data, and test data as well as downstream tasks. The paper begins by offering an introduction and brief summary of current GPT- and BERT-style LLMs. It then delves into the influence of pre-training data, training data, and test data on the performance of LLMs. The authors emphasize the importance of understanding the use cases as well as non-use cases of large language models for different NLP tasks such as knowledge-intensive tasks, traditional natural language understanding tasks, natural language generation tasks, emergent abilities, and considerations for specific tasks. To illustrate the practical applications and limitations of LLMs in real-world scenarios, the authors present various use cases and non-use cases. They also highlight the significance of data quality and discuss specific challenges associated with each NLP task. Additionally, the paper explores the impact of spurious biases on LLMs and addresses other essential considerations like efficiency, cost, latency to ensure a comprehensive understanding of deploying LLMs in practice. The ultimate goal of this comprehensive guide is to provide researchers and practitioners with valuable insights and best practices for effectively working with LLMs. By doing so it enables successful implementation across a wide range of NLP tasks. The paper concludes by providing a curated list of practical guide resources for LLMs that are regularly updated. Overall this paper serves as an invaluable resource for anyone involved in working with Large Language Models (LLMs), providing them with a detailed understanding of these models' capabilities, limitations best practices for usage across various NLP tasks while addressing important considerations for deployment.