Results of the summarizing process for the arXiv paper: 2304.13712v1

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.

• 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

This is a book that helps people who work with big computer programs that understand and use language. It talks about different ways to use these programs and how they are made. It also explains how the information used to make these programs can affect how well they work. The book also tells us when it's good to use these programs and when it's not. It talks about real-life situations where we can use these programs, but also says there are some things they can't do. The book also talks about how important it is to have good information for these programs and the problems that can happen if there are mistakes in the information. It also explains how using these programs can sometimes show unfair ideas or beliefs. Finally, the book gives advice on how to make sure these programs work well and gives a list of other helpful resources." Definitions- Comprehensive: including everything or almost everything - Practical: useful in real situations - Guide: a book or document that gives you information or instructions about something - Practitioners: people who do a job or activity regularly - End-users: people who actually use something - Large Language Models (LLMs): big computer programs that understand and use language - NLP tasks: activities related to understanding and using language with computers

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

Large Language Models (LLMs) have become increasingly popular in natural language processing (NLP) tasks. This paper, titled "Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond," provides a comprehensive guide for practitioners and end-users who work with these models. It offers 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.

Introduction to GPT-style & BERT-style LLMs

The paper begins by offering an introduction to current GPT-style Large Language Models (LLM) such as OpenAI's GPT-2 model and Google's BERT model. It then delves into the influence of pre-training data, training data, and test data on the performance of these models. The authors emphasize that it is essential to understand both use cases as well as non-use cases for different NLP tasks when working with large language models.

Use Cases & Non Use Cases

To illustrate the practical applications and limitations of LLMs in real world scenarios, the authors present various use cases which include knowledge intensive tasks such as question answering systems; traditional natural language understanding tasks like sentiment analysis; natural language generation tasks like summarization; emergent abilities like dialogue systems; etc., along with their respective non use cases. They also highlight how important it is to consider quality of data while working with large language models across different NLP tasks. Additionally they discuss specific challenges associated with each task such as spurious biases or efficiency considerations that need to be taken into account before deploying them in practice.

Conclusion & Resources

The ultimate goal of this comprehensive guide is to provide researchers and practitioners with valuable insights about effectively working with Large Language Models (LLMs). By doing so it enables successful implementation across a wide range of NLP tasks while addressing important considerations for deployment like cost or latency constraints . The paper concludes by providing a curated list of practical guide resources for LLMs that are regularly updated which can be used by anyone involved in working with these powerful tools. 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