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}.

翻译：本文为从事下游自然语言处理（NLP）任务的研究人员和实践者提供了一份全面且实用的指南，针对大型语言模型（LLMs）的使用展开讨论。我们从模型、数据和下游任务三个维度，深入探讨了LLMs的应用方式。首先，我们介绍了当前GPT类和BERT类LLMs的基本情况并进行了简要总结。随后，讨论了预训练数据、训练数据和测试数据的影响。最重要的是，我们详细探讨了大型语言模型在各种自然语言处理任务中的使用与非使用场景，包括知识密集型任务、传统自然语言理解任务、自然语言生成任务、涌现能力，以及特定任务的注意事项。我们通过呈现多种使用与非使用案例，阐明了LLMs在实际场景中的应用与局限性。同时，我们试图理解数据的重要性以及每个NLP任务面临的特定挑战。此外，我们探究了虚假偏差对LLMs的影响，并深入考虑了效率、成本和延迟等其他关键因素，以确保对LLMs实际部署的全面理解。本综合指南旨在为研究人员和实践者提供与LLMs协作的宝贵见解和最佳实践，从而助力这些模型在广泛的NLP任务中成功应用。一份定期更新的LLMs实用指南资源列表可在\url{https://github.com/Mooler0410/LLMsPracticalGuide}获取。