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：本文为从业者与最终用户提供了一份全面且实用的指南，主要面向在自然语言处理（NLP）下游任务中运用大语言模型（LLMs）的实践者。我们从模型、数据与下游任务的视角出发，对LLMs的使用展开了讨论与深度剖析。首先，我们简要介绍并总结了当前GPT类与BERT类LLMs。随后，探讨了预训练数据、训练数据与测试数据的影响。最为关键的是，我们针对LLMs在各类NLP任务（如知识密集型任务、传统自然语言理解任务、自然语言生成任务、新兴能力及特定任务考量）中的适用与不适用场景进行了详细论述。通过呈现多种适用与不适用案例，阐明LLMs在现实场景中的应用实践与局限性。我们还试图理解数据的重要性以及每项NLP任务所伴随的特定挑战。此外，我们探究了虚假偏差对LLMs的影响，并深入探讨了效率、成本与延迟等其他关键考量因素，以确保读者全面理解LLMs在实际部署中的要义。这份综合指南旨在为研究人员与从业者提供与LLMs协作的宝贵见解与最佳实践，从而推动这些模型在广泛NLP任务中的成功实施。一份持续更新的LLMs实用指南资源清单可在\url{https://github.com/Mooler0410/LLMsPracticalGuide}获取。