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 language models. 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, 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 https://github.com/Mooler0410/LLMsPracticalGuide. An LLMs evolutionary tree, editable yet regularly updated, can be found at llmtree.ai.

本文为在下游自然语言处理 (NLP) 任务中使用大型语言模型 (LLM) 的从业者和最终用户提供了全面且实用的指南。我们从模型、数据和下游任务的角度对法学硕士的使用进行讨论和见解。首先，我们对当前的语言模型进行介绍和简要总结。然后，我们讨论预训练数据、训练数据和测试数据的影响。最重要的是，我们详细讨论了大语言模型在各种自然语言处理任务中的使用和不使用案例，例如知识密集型任务、传统自然语言理解任务、生成任务、涌现能力以及特定的考虑因素任务。我们提出了各种用例和非用例来说明法学硕士在现实场景中的实际应用和局限性。我们还尝试了解数据的重要性以及与每个 NLP 任务相关的具体挑战。此外，我们还探讨了虚假偏差对法学硕士的影响，并深入研究了其他基本考虑因素，例如效率、成本和延迟，以确保全面了解在实践中部署法学硕士。本综合指南旨在为研究人员和从业者提供与法学硕士合作的宝贵见解和最佳实践，从而使这些模型能够在广泛的 NLP 任务中成功实施。定期更新的法学硕士实用指南资源精选列表可在 https://github.com/Mooler0410/LLMsPracticalGuide 找到。您可以在 llmtree.ai 上找到可编辑且定期更新的法学硕士进化树。