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 https://github.com/Mooler0410/LLMsPracticalGuide.

When deploying LLMs for downstream tasks, we often face challenges stemming from distributional differences between
the test/user data and that of the training data. These disparities may encompass domain shifts [132], out-of-distribution
variations [31], or even adversarial examples [82]. Such challenges significantly hinder fine-tuned modes’ effectiveness
in real-world applications.

However, LLMs perform quite well facing such scenarios because they do not have an explicit fitting process. Moreover,
recent advancements have further enhanced the ability of language models in this regard. The Reinforcement Learning
from Human Feedback (RLHF) method has notably enhanced LLMs’ generalization capabilities [77].