Introduction to large language models

Introduction

Hey there! Welcome to the introduction to large language models. My name is John Ewald, and I'm a training developer here at Google Cloud. In this course, you will learn about the definition of large language models (LLMs), their use cases, prompt tuning, and Google's gen AI development tools.

Large language models are a subset of deep learning. They are large, general-purpose models that can be pre-trained and then fine-tuned for specific purposes. These models are trained to solve common language problems such as text classification, question answering, document summarization, and text generation. They can then be tailored to solve specific problems in various industries using smaller field data sets.

The term "large" in large language models refers to the size of the training data set and the parameter count. These models are trained with enormous amounts of data, often at the petabyte scale, and have a tremendous number of parameters, which define their abilities and skills in solving language problems.

Large language models are general purpose, meaning they can be used to solve common problems across different fields. This is possible because of the commonality of human language and the resource restrictions that prevent everyone from training such large models. Pre-training and fine-tuning are the processes involved in making these models practical. First, the models are pre-trained on a large data set for a general purpose, and then they are fine-tuned for specific aims using smaller data sets.

The benefits of using large language models are significant. A single model can be used for different tasks, such as language translation, sentence completion, text classification, and question answering. These models require minimal field training data when tailored to a specific problem, and their performance continues to improve with more data and parameters.

Let's take a closer look at Google's Pathways Language Model (Palm) as an example. Palm is a 540 billion parameter model that achieves state-of-the-art performance across multiple language tasks. It leverages the new Pathways system, which allows efficient training of a single model across multiple TPU V4 pods. Palm is a Transformer model, consisting of an encoder and decoder. The encoder encodes the input sequence, while the decoder learns how to decode representations for a relevant task.

Large language models have evolved from traditional programming to neural networks and now generative models. In traditional programming, we had to hard code rules, but with neural networks, we could predict outcomes based on training examples. Generative models, such as Palm or Lambda, allow users to generate their own content by leveraging large data sets and pre-trained models. You can simply ask a question or provide a prompt, and the model will generate the desired output.

Keywords:

• Large language models
• Pre-training and fine-tuning
• Benefits of large language models
• Google's Pathways Language Model (Palm)
• Transformer models
• Generative models

FAQ:

1. What are large language models?

   • Large language models are large, general-purpose models that can be pre-trained and fine-tuned for specific purposes. They are used to solve common language problems and can be tailored to specific fields and tasks.

2. How do large language models differ from traditional programming?

   • In traditional programming, rules are hard-coded to distinguish different outcomes. In large language models, the models are trained on large data sets and can generate content based on prompts or questions without explicit rules.

3. What is prompt design?

   • Prompt design is the process of creating a prompt that is clear, concise, and informative. It is essential in natural language processing to ensure accurate and meaningful responses from the models.

4. How can large language models be fine-tuned for specific tasks?

   • Large language models can be fine-tuned by training them on smaller data sets specific to the desired task or field. This allows customization of the model's response and improves performance in the given domain.

5. What tools does Google offer for developing and deploying large language models?

   • Google provides tools like Generative AI Studio, Generative AI App Builder, and the Palm API. These tools help developers explore, customize, and deploy generative AI models easily and efficiently.

Hope this article helps you understand the basics of large language models and how they can be used in various applications. If you have any further questions, feel free to ask!