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.
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
This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (NLP) tasks. We provide discussions and insights into the usage of LLMs from the perspectives of models, data, and downstream tasks. Firstly, we offer an introduction and brief summary of current GPT- and BERT-style LLMs. Then, we discuss the influence of pre-training data, training data, and test data. Most importantly, we provide a detailed discussion about the use 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, emergent abilities, and considerations for specific tasks.We present various use cases and non-use cases to illustrate the practical applications and limitations of LLMs in real-world scenarios. We also try to understand the importance of data and the specific challenges associated with each NLP task. Furthermore, we explore the impact of spurious biases on LLMs and delve into other essential considerations, such as efficiency, cost, and latency, to ensure a comprehensive understanding of deploying LLMs in practice. This comprehensive guide aims to provide researchers and practitioners with valuable insights and best practices for working with LLMs, thereby enabling the successful implementation of these models in a wide range of NLP tasks. A curated list of practical guide resources of LLMs, regularly updated, can be found at \url{https://github.com/Mooler0410/LLMsPracticalGuide}.
