大型語言模型的使用方式分析

Table of Contents

1. Introduction

   • Overview of large-Scale language models
   • Two different expectations for large-scale language models

2. Fintuning

   • Introduction to Fintuning
   • Comparison between GPT and BERT
   • Text completion with BERT
   • The concept of Fintuning
   • Modifications and improvements for task-specific models
   • Adding plugins and external modules
   • The benefits of Fintuning

3. Prompt

   • Introduction to Prompt
   • ChatGPT as a generalist model
   • Historical background of Prompt
   • Using human language to control language models
   • Prompt as a bridge between humans and machines
   • ChatGPT as a versatile model
   • Natural Language Decathlon
   • Ask Me Anything concept

4. Pros and Cons of Specialists and Generalists

   • Advantages of specialists
   • Advantages of generalists
   • Limitations of specialists and generalists
   • The Quest for a balance

5. Efficient Fine-tuning

   • Introduction to efficient fine-tuning
   • The concept of adapter
   • Benefits of adapters
   • Efficient fine-tuning versus traditional fine-tuning
   • Efficiently training a task-specific model

6. Conclusion

   • Achieving a balance between specialists and generalists
   • The future of large-scale language models
   • Final thoughts

Article

Introduction

Large-scale language models have two different expectations in history, leading to very different results. These expectations correspond to two different technologies: Fintuning and Prompt. GPT, a well-known language model, focuses on text completion and is widely familiarized. Another prominent language model, BERT, introduced by Google at the end of 2018, specializes in text filling and has subtle differences from GPT. While GPT completes incomplete sentences, BERT fills in the gaps in a sentence.

To better understand the concept of using large-scale language models, imagine a Diagram with a mouse riding on an elephant's head, controlling the elephant with reins. The elephant represents the language model, which has absorbed extensive information from the internet but lacks direction. The mouse represents the human user who guides the elephant in the desired direction. This analogy illustrates the need for human intervention to guide large-scale language models.

Fintuning

Fintuning refers to the process of making adjustments to a language model to transform it into a specialist for a specific task. For example, by making specific modifications to a language model like GPT, we can enable it to excel at translation. By providing it with a sentence, it can accurately translate it from Chinese to English. Fintuning offers the AdVantage of tailoring the language model to a specific task, allowing it to outperform general-purpose systems in that particular area.

Efficient fine-tuning is a technique that avoids training the entire language model from scratch. Instead, it focuses on fine-tuning specific components, such as adding plugins and external modules to the model. This approach enables rapid development of new functionalities without the need to write complex code. By utilizing language Prompts, human users can instruct the language model to perform specific tasks, such as generating summaries or translating text.

Prompt

The concept of Prompt revolves around giving direct instructions to the language model using human language. ChatGPT exemplifies this expectation of language models by aiming to become a generalist capable of performing various tasks. The idea of making a language model a generalist is not Novel; it has been a long-standing dream. The Notion that all natural language processing problems boil down to question-answering tasks has long been proposed.

In the past, the term "Prompt" was not commonly used, but the concept was present. Prompt can be considered equivalent to the term "Question" in previous discussions. Rather than manipulating the language model through programming languages, Prompt allows for human-like language instructions. With ChatGPT, users can directly control the model using their own language, making it a versatile tool.

Pros and Cons of Specialists and Generalists

Specialist models have the advantage of focus and specialization. By concentrating on a specific task, such as translation, these models can often outperform generalist models like ChatGPT within their domain. However, the drawback is that specialist models are limited in their ability to handle multiple tasks and lack the versatility of generalist models.

Generalist models, on the other HAND, embody the human imagination and the desired capabilities of artificial intelligence. They are the subject of discussions and news articles, attracting Attention and stimulating conversations. Generalist models like ChatGPT have the advantage of being able to perform various tasks by receiving instructions in human language. By using pre-trained generalist models, developers can quickly develop new functionalities without starting from scratch.

Efficient Fine-tuning

Efficient fine-tuning involves the use of adapters, which are additional modules inserted into language models. This technique allows for task-specific fine-tuning of the adapter without modifying the entire language model. By using adapters, only a small number of parameters need to be stored while achieving task-specific capabilities. This approach offers a more efficient alternative to traditional fine-tuning, which requires storing separate models for each task.

By using efficient fine-tuning, language models like GPT-3 can be further enhanced for specific tasks. The advantages of adapters lie in their flexibility and efficient implementation. With a vast variety of adapter placements and configurations, developers can experiment to find the most suitable approach for their applications.

Conclusion

The expectations for language models have diverged into two paths: specialization and generalization. Fintuning allows language models to excel in specific tasks by making task-specific adjustments and fine-tuning. On the other hand, Prompt enables language models like ChatGPT to become generalists, capable of performing various tasks through human-like instructions. Both approaches have their advantages and drawbacks, and achieving a balance between specialists and generalists is essential for future advancements in large-scale language models.