quality text for various applications.

• Knowledge-intensive tasks. Leverage the extensive knowledge stored in LLMs for tasks requiring domain-

specific expertise or general world knowledge.

• Reasoning ability. Understand and harness the reasoning capabilities of LLMs to improve decision-making

and problem-solving in various contexts.

2 PRACTICAL GUIDE FOR MODELS
This section provides a brief introduction to state-of-the-art LLMs. These models differ in their training strategies,
model architectures, and use cases. To provide a clearer understanding of the LLM landscape, we categorize them into
two types: encoder-decoder or encoder-only language models and decoder-only language models. In Figure 1, we show
the detailed evolution process of language models. From the evolutionary tree, we make the following interesting
observations:
Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond