Introduction to large language models

Summary

In 'Introduction to Large Language Models' by John Ewald, viewers are introduced to LLMs, a subset of deep learning. The course covers defining LLMs, their use cases, prompt tuning, and Google's Gen AI development tools. LLMs are pre-trained on vast data sets and fine-tuned for specific tasks, offering benefits like versatility across tasks and minimal field training data. The video also discusses the evolution from traditional programming to generative AI, highlighting models like PaLM and their capabilities in natural language processing. The course concludes with tools like Generative AI Studio and Gen AI App Builder, which facilitate the creation and deployment of AI models without extensive coding.

Takeaways

• 💡 Large Language Models (LLMs) are a subset of deep learning used for a variety of language-related tasks.
• 🌐 LLMs and generative AI intersect, both being part of the broader field of deep learning, with generative AI focusing on producing new content.
• 🐶 LLMs are trained like dogs learning basic commands and then can be fine-tuned for specialized tasks, much like service dogs receive additional training.
• 📈 The 'large' in LLMs refers to the massive training data sets and the high parameter count, which define the model's capabilities.
• 🔧 General-purpose LLMs are designed to handle common language tasks across industries, making them versatile tools.
• 🔑 The benefits of LLMs include their ability to perform various tasks, require minimal field training data, and improve with more data and parameters.
• 🚀 Google's PaLM, a 540 billion-parameter model, exemplifies state-of-the-art performance in language tasks and utilizes the new Pathways system for efficient training.
• 🔄 The transition from traditional programming to neural networks to generative models represents a shift from hard-coded rules to learning from data to creating new content.
• ❓ LLMs can be used for question-answering systems, which can answer a wide range of questions without extensive domain knowledge, unlike traditional QA models.
• 🛠 Prompt design and engineering are critical for effectively using LLMs, with design focusing on clarity and engineering on performance improvement.
• 🔄 There are three types of LLMs: generic, instruction-tuned, and dialogue-tuned, each requiring different prompting strategies for optimal performance.

Q & A

What is the main focus of the course 'Introduction to Large Language Models'?

The course focuses on teaching how to define large language models (LLMs), describe their use cases, explain prompt tuning, and describe Google's Gen AI development tools.

How are large language models (LLMs) related to generative AI?

LLMs and generative AI intersect and are both part of deep learning. Generative AI is a type of AI that can produce new content including text, images, audio, and synthetic data.

What does the term 'large' signify in the context of large language models?

In the context of LLMs, 'large' signifies two things: the enormous size of the training data set, sometimes at the petabyte scale, and the parameter count, which refers to the memories and knowledge the machine learned from the model training.

What is the difference between pre-trained and fine-tuned models in the context of LLMs?

Pre-trained models are trained for general purposes to solve common language problems, while fine-tuned models are tailored to solve specific problems in different fields using a relatively small size of field data sets.

What are the benefits of using large language models?

The benefits include the ability to use a single model for different tasks, requiring minimal field training data, and continuous performance improvement with added data and parameters.

Can you provide an example of a large language model developed by Google?

An example is PaLM (Pathways Language Model), a 540 billion-parameter model that achieves state-of-the-art performance across multiple language tasks.

What is the significance of the transformer model architecture in LLMs?

A transformer model consists of an encoder and a decoder. The encoder encodes the input sequence and passes it to the decoder, which learns how to decode the representations for a relevant task.

How does prompt design differ from prompt engineering in the context of LLMs?

Prompt design is the process of creating a prompt tailored to a specific task, while prompt engineering is more specialized, involving creating prompts designed to improve performance, which may include using domain-specific knowledge or providing examples of desired output.

What are the three types of large language models mentioned in the script?

The three types are generic language models, instruction-tuned models, and dialogue-tuned models.

What is chain of thought reasoning in the context of LLMs?

Chain of thought reasoning is the observation that models are better at getting the right answer when they first output text that explains the reason for the answer.

How does Vertex AI assist in the development and deployment of generative AI models?

Vertex AI provides task-specific foundation models and tools for fine-tuning, deploying models to production, and monitoring their performance, making it easier for developers to create and deploy generative AI models.

Outlines

00:00

📚 Introduction to Large Language Models

John Ewald introduces the course on Large Language Models (LLMs), explaining that LLMs are a part of deep learning and intersect with generative AI. LLMs are pre-trained on vast datasets and can be fine-tuned for specific tasks. The analogy of training a dog is used to illustrate the concept of pre-training and fine-tuning. The benefits of LLMs include their versatility across different tasks, minimal requirement for field training data, and continuous performance improvement with more data and parameters. The introduction also highlights Google's PaLM, a state-of-the-art LLM with 540 billion parameters, showcasing the advancements in AI architecture.

05:01

🤖 Understanding LLM Development and Applications

This section compares LLM development with traditional machine learning, emphasizing the ease of use of LLMs through prompt design, which eliminates the need for expertise, training examples, or model training. The video provides examples of question answering, a subfield of natural language processing, and how LLMs can generate free text based on context without domain knowledge. It also discusses the differences between prompt design and prompt engineering, highlighting their importance in natural language processing. The paragraph concludes with an overview of the three types of LLMs: generic, instruction-tuned, and dialogue-tuned, each requiring different prompting strategies.

10:02

🔍 Exploring LLM Tuning and Practical Applications

The paragraph delves into the concept of tuning LLMs to make them more reliable for specific tasks. It introduces task-specific foundation models provided by Vertex AI and explains how tuning can customize model responses. The paragraph also touches on parameter-efficient tuning methods, which allow for model tuning without altering the base model. Generative AI Studio and Gen AI App Builder are introduced as tools for developers to explore, customize, and deploy generative AI models on Google Cloud. The paragraph concludes with a mention of the PaLM API, which allows developers to experiment with Google's large language models and Gen AI tools.

15:04

🛠️ Tools for LLM Development and Deployment

The final paragraph outlines the tools available for developers to train, deploy, and monitor ML models effectively. It introduces the Maker Suite, which includes a model-training tool, a model-deployment tool, and a model-monitoring tool. Each tool is designed to facilitate different aspects of the ML development lifecycle, from training models using various algorithms to deploying them with multiple options and monitoring their performance in production. The paragraph wraps up the course by thanking viewers for their attention.

Keywords

💡Large Language Models (LLMs)

Large Language Models, or LLMs, refer to advanced AI systems designed to understand and generate human-like text based on vast amounts of data. They are a subset of deep learning and are characterized by their ability to be pre-trained on general language tasks and then fine-tuned for specific applications. In the video, LLMs are central to the discussion, with examples given of their use in various industries and their ability to perform tasks like text classification and question answering.

💡Generative AI

Generative AI is a type of artificial intelligence that can create new content, including text, images, audio, and synthetic data. It intersects with LLMs in the sense that both involve creating outputs based on learned patterns. The video discusses how generative AI can produce new content, and how LLMs fit into this broader category of AI technologies.

💡Pre-trained and Fine-tuned

This concept in the video script refers to the process of initially training a model on a large dataset to perform general tasks and then adjusting it with a smaller, specialized dataset to perform specific tasks. The analogy of training a dog for general commands and then additional training for specialized service is used to illustrate this point. In the context of LLMs, pre-training provides a broad understanding of language, and fine-tuning tailors this understanding to specific industry needs.

💡Parameters

In machine learning, parameters are the internal values that a model learns during training, which are used to make predictions or decisions. They are likened to the 'memories and knowledge' of the model. The video emphasizes the importance of the scale of parameters in LLMs, with large numbers indicating a model's capacity for complex tasks and learning.

💡General Purpose

This term is used to describe the versatility of LLMs, which are capable of solving a wide array of common language problems due to their training on diverse datasets. The video explains that the commonality of human language and resource restrictions lead to the development of general-purpose models that can be adapted to various tasks and domains.

💡Few-shot and Zero-shot Learning

Few-shot learning refers to training a model with a small amount of data, while zero-shot learning implies that a model can recognize and perform tasks without explicit training on that task. The video highlights the efficiency of LLMs in these scenarios, showcasing their ability to perform well even with minimal domain-specific training data.

💡PaLM (Pathways Language Model)

PaLM, or Pathways Language Model, is a specific example of an LLM developed by Google, mentioned in the video as having 540 billion parameters. It represents the state-of-the-art in language modeling, demonstrating high performance across multiple language tasks. The video uses PaLM to illustrate the capabilities of modern LLMs and their architectural advancements.

💡Transformer Model

A transformer model is a type of deep learning architecture that processes sequences of data, such as text. It consists of encoder and decoder components that enable the model to understand and generate text. The video explains that PaLM is a dense decoder-only transformer model, highlighting the role of transformer architecture in the advanced capabilities of LLMs.

💡Prompt Design

Prompt design is the process of creating clear, concise, and informative prompts for LLMs to elicit desired responses. The video emphasizes the importance of prompt design in natural language processing, especially in the context of LLM development where it replaces the need for extensive training data and expertise.

💡Prompt Engineering

Prompt engineering is a more specialized aspect of creating prompts, focusing on improving the performance of LLMs. It may involve using domain-specific knowledge or providing examples to guide the model's output. The video distinguishes between prompt design, which is more general, and prompt engineering, which is necessary for high-accuracy tasks.

💡Chain of Thought Reasoning

This concept refers to the observation that AI models, including LLMs, often provide more accurate answers when they first output a chain of reasoning or explanation. The video gives an example where a model is more likely to give the correct answer to a math problem after initially explaining the steps involved, demonstrating the importance of this reasoning process in achieving accurate outputs.

Highlights

Introduction to Large Language Models (LLMs) and their significance in solving common language problems such as text classification, question answering, document summarization, and text generation.

LLMs are pre-trained for general purposes and fine-tuned for specific fields like retail, finance, and entertainment using smaller datasets.

The term 'large' in LLMs refers to both the enormous size of training datasets (often at the petabyte scale) and the parameter count (which can reach billions).

Pre-trained LLMs offer versatility, allowing them to be adapted to specific tasks with minimal domain training data, making them useful in few-shot or zero-shot scenarios.

PaLM (Pathways Language Model) is an example of a state-of-the-art LLM developed by Google, featuring 540 billion parameters and capable of performing a wide range of language tasks.

PaLM is built on Google's new Pathways system, which allows efficient training of large models across multiple TPU V4 pods.

Generative AI models can produce new content, including text, images, audio, and synthetic data, expanding beyond traditional machine learning models.

Generative models like PaLM and LaMDA can generate content and respond to user queries without requiring extensive domain knowledge or training data.

Prompt design and prompt engineering are essential concepts in LLMs, focusing on creating clear and informative prompts to optimize the model's performance.

LLMs are capable of performing tasks such as language translation, text classification, and sentiment analysis with minimal input data.

Tuning and fine-tuning LLMs enable further customization for specific tasks, with methods like parameter-efficient tuning allowing this without duplicating the entire model.

Vertex AI provides task-specific foundation models for different use cases, such as sentiment analysis or vision tasks like occupancy analytics.

Generative AI Studio and Gen AI App Builder allow developers to create and deploy generative AI models and applications without extensive coding knowledge.

The PaLM API, combined with Maker Suite, provides an accessible interface for developers to experiment with LLMs and generative AI tools.

LLMs have practical limitations, but task-specific tuning, as offered by Vertex AI, can improve their reliability for specialized use cases.

Transcripts

JOHN EWALD: Hello, and welcome to Introduction

to Large Language Models.

My name is John Ewald, and I'm a training developer here

at Google Cloud.

In this course, you learn to define large language

models, or LLMs, describe LLM use cases,

explain prompt tuning, and describe Google's Gen AI

development tools.

Large language models, or LLMs, are a subset of deep learning.

To find out more about deep learning,

see our Introduction to Generative AI course video.

LLMs and generative AI intersect and they are

both a part of deep learning.

Another area of AI you may be hearing a lot about

is generative AI.

This is a type of artificial intelligence that

can produce new content, including text, images, audio,

and synthetic data.

So what are large language models?

Large language models refer to large general-purpose language

models that can be pre-trained and then fine

tuned for specific purposes.

What do pre-trained and fine tuned mean?

Imagine training a dog.

Often, you train your dog basic commands

such as sit, come, down, and stay.

These commands are normally sufficient for everyday life

and help your dog become a good canine citizen.

However, if you need a special service dog such as a police

dog, a guide dog, or a hunting dog, you add special trainings.

The similar idea applies to large language models.

These models are trained for general purposes

to solve common language problems such as text

classification, question answering, document

summarization, and text generation across industries.

The models can then be tailored to solve specific problems

in different fields such as retail, finance,

and entertainment using a relatively small size of field

data sets.

Let's further break down the concept

into three major features of large language models.

Large indicates two meanings.

First is the enormous size of the training data set,

sometimes at the petabyte scale.

Second, it refers to the parameter count.

In ML, parameters are often called hyperparameters.

Parameters are basically the memories and the knowledge

that the machine learned from the model training.

Parameters define the skill of a model

in solving a problem such as predicting text.

General purpose means that the models

are sufficient to solve common problems.

Two reasons lead to this idea.

First is the commonality of a human language regardless

of the specific tasks.

And second is the resource restriction.

Only certain organizations have the capability

to train such large language models with huge data sets

and a tremendous number of parameters.

How about letting them create fundamental language models

for others to use?

This leads to the last point, pre-trained and fine tuned,

meaning to pre-train a large language

model for a general purpose with a large data set

and then fine tune it for specific aims with a much

smaller data set.

The benefits of using large language models

are straightforward.

First, a single model can be used for different tasks.

This is a dream come true.

These large language models that are

trained with petabytes of data and generate billions

of parameters are smart enough to solve different tasks,

including language translation, sentence completion, text

classification, question answering, and more.

Second, large language models require minimal field training

data when you tailor them to solve your specific problem.

Large language models obtain decent performance

even with little domain training data.

In other words, they can be used for few shot or even

zero-shot scenarios.

In machine learning, few shot refers

to training a model with minimal data,

and zero shot implies that a model can recognize

things that have not explicitly been taught in the training

before.

Third, the performance of large language models

is continuously growing when you add more data and parameters.

Let's take PaLM as an example.

In April 2022, Google released PaLM,

short for Pathways Language Model, a 540 billion-parameter

model that achieves a state of the art performance

across multiple language tasks.

PaLM is a dense decoder-only transformer model.

It has 540 billion parameters.

It leverages the new pathways system,

which has enabled Google to efficiently

train a single model across multiple TPU V4 pods.

Pathway is a new AI architecture that

will handle many tasks at once, learn new tasks quickly,

and reflect a better understanding of the world.

The system enables PaLM to orchestrate distributed

computation for accelerators.

We previously mentioned that PaLM is a transformer model.

A transformer model consists of encoder and decoder.

The encoder encodes the input sequence

and passes it to the decoder, which

learns how to decode the representations

for a relevant task.

We've come a long away from traditional programming

to neural networks to generative models.

In traditional programming, we used

to have to hard code the rules for distinguishing a cat--

type, animal; legs, four; ears, two; fur, yes;

likes yarn, catnip.

In the wave of neural networks, we

could give the network pictures of cats and dogs and ask,

is this a cat?

And it would predict a cat.

In the generative wave, we as users

can generate our own content, whether it be text, images,

audio, video, or other.

For example, models like PaLM, or LaMDA,

or Language Model for Dialogue Applications,

ingest very, very large data from multiple sources

across the internet and build foundation language models

we can use simply by asking a question,

whether typing it into a prompt or verbally

talking into the prompt.

So when you ask it what's a cat, it

can give you everything it has learned about a cat.

Let's compare LLM development using pre-trained models

with traditional ML development.

First, with LLM development, you don't need to be an expert.

You don't need training examples.

And there is no need to train a model.

All you need to do is think about prompt design, which

is the process of creating a prompt that is clear, concise,

and informative.

It is an important part of natural language processing.

In traditional machine learning, you

need training examples to train a model.

You also need compute time and hardware.

Let's take a look at an example of a text generation use case.

Question answering, or QA, is a subfield of natural language

processing that deals with the task of automatically answering

questions posed in natural language.

QA systems are typically trained on a large amount

of text and code.

And they are able to answer a wide range of questions,

including factual, definitional, and opinion-based questions.

The key here is that you need domain knowledge

to develop these question-answering models.

For example, domain knowledge is required

to develop a question-answering model for customer

support, or health care, or supply chain.

Using generative QA, the model generates free text

directly based on the context.

There is no need for domain knowledge.

Let's look at three questions given to Bard,

a large language model chat bot developed by Google AI.

Question one.

"This year's sales are $100,000.

Expenses are $60,000.

How much is net profit?"

Bard first shares how net profit is calculated, then

performs the calculation.

Then Bard provides the definition of net profit.

Here's another question.

Inventory on hand is 6,000 units.

A new order requires 8,000 units.

How many units do I need to fill to complete the order?

Again, Bard answers the question by performing the calculation.

And our last example, we have 1,000 sensors

in 10 geographic regions.

How many sensors do we have on average in each region?

Bard answers the question with an example

on how to solve the problem and some additional context.

In each of our questions, a desired response was obtained.

This is due to prompt design.

Prompt design and prompt engineering

are two closely-related concepts in natural language processing.

Both involve the process of creating

a prompt that is clear, concise, and informative.

However, there are some key differences between the two.

Prompt design is the process of creating a prompt that

is tailored to the specific task that this system is

being asked to perform.

For example, if the system is being

asked to translate a text from English to French,

the prompt should be written in English

and should specify that the translation should

be in French.

Prompt engineering is the process

of creating a prompt that is designed

to improve performance.

This may involve using domain-specific knowledge,

providing examples of the desired output,

or using keywords that are known to be

effective for the specific system.

Prompt design is a more general concept,

while prompt engineering is a more specialized concept.

Prompt design is essential, while prompt engineering

is only necessary for systems that

require a high degree of accuracy or performance.

There are three kinds of large language models,

generic language models, instruction tuned,

and dialogue tuned.

Each needs prompting in a different way.

Generic language models predict the next word

based on the language in the training data.

This is an example of a generic language model.

The next word is a token based on the language in the training

data.

In this example, "the cat sat on,"

the next word should be "the."

And you can see that "the" is the most likely next word.

Think of this type as an autocomplete in search.

In instruction tuned, the model is

trained to predict a response to the instructions

given in the input.

For example, summarize a text of X,

generate a poem in the style of X,

give me a list of keywords based on semantic similarity for X.

And in this example, classify the text

into neutral, negative, or positive.

In dialogue tuned, the model is trained to have a dialogue

by the next response.

Dialogue-tuned models are a special case

of instruction tuned where requests are typically framed

as questions to a chat bot.

Dialogue tuning is expected to be

in the context of a longer back and forth conversation,

and typically works better with natural question-like

phrasings.

Chain of thought reasoning is the observation

that models are better at getting the right answer when

they first output text that explains

the reason for the answer.

Let's look at the question.

Roger has five tennis balls.

He buys two more cans of tennis balls.

Each can has three tennis balls.

How many tennis balls does he have now?

This question is posed initially with no response.

The model is less likely to get the correct answer directly.

However, by the time the second question is asked,

the output is more likely to end with the correct answer.

A model that can do everything has practical limitations.

Task-specific tuning can make LLMs more reliable.

Vertex AI provides task-specific foundation models.

Let's say you have a use case where

you need to gather sentiments, or how

your customers are feeling about your product or service.

You can use the classification task

sentiment analysis task model.

Same for vision tasks.

If you need to perform occupancy analytics,

there is a task-specific model for your use case.

Tuning a model enables you to customize the model response

based on examples of the task that you

want the model to perform.

It is essentially the process of adapting a model

to a new domain, or set of custom use cases,

by training the model on new data.

For example, we may collect training data

and tune the model specifically for the legal or medical

domain.

You can also further tune the model by fine tuning

where you bring your own data set

and retrain the model by tuning every weight in the LLM.

This requires a big training job and hosting

your own fine-tuned model.

Here's an example of a medical foundation model trained

on health care data.

The tasks include question answering, image analysis,

finding similar patients, and so forth.

Fine tuning is expensive and not realistic in many cases.

So are there more efficient methods of tuning?

Yes.

Parameter-efficient tuning methods, or PETM,

are methods for tuning a large language

model on your own custom data without duplicating the model.

The base model itself is not altered.

Instead, a small number of add-on layers

are tuned, which can be swapped in and out at inference time.

Generative AI Studio lets you quickly explore and customize

generative AI models that you can

leverage in your applications on Google Cloud.

Generative AI Studio helps developers create and deploy

generative AI models by providing

a variety of tools and resources that

make it easy to get started.

For example, there's a library of pre-trained models,

a tool for fine tuning models, a tool for deploying models

to production, and a community forum for developers

to share ideas and collaborate.

Generative AI App Builder lets you

create Gen AI apps without having to write any code.

Gen AI App Builder has a drag-and-drop interface

that makes it easy to design and build

apps, a visual editor that makes it easy to create and edit

app content, a built-in search engine that allows users

to search for information within the app,

and a conversational AI engine that

allows users to interact with the app using natural language.

You can create your own chat bots, digital assistants,

custom search engines, knowledge bases, training applications,

and more.

PaLM API lets you test and experiment

with Google's large language models and Gen AI tools.

To make prototyping quick and more accessible,

developers can integrate PaLM API with Maker Suite

and use it to access the API using a graphical user

interface.

The suite includes a number of different tools

such as a model-training tool, a model-deployment tool,

and a model-monitoring tool.

The model-training tool helps developers train ML models

on their data using different algorithms.

The model deployment tool helps developers deploy ML models

to production with a number of different deployment options.

And the model-monitoring tool helps

developers monitor the performance of their ML models

in production using a dashboard and a number

of different metrics.

That's all for now.

Thanks for watching this course, Introduction to Large Language

Models.