Introduction to Large Language Models

Summary

This video script offers an insightful exploration into large language models (LLMs), a subset of deep learning capable of pre-training and fine-tuning for specific tasks. The presenter, a Google Cloud engineer, explains the concept of LLMs, their use cases, the process of prompt tuning, and Google's AI development tools. The script delves into the benefits of using LLMs, including their adaptability, minimal training data requirements, and continuous performance improvement. Examples of LLM applications, such as question answering and sentiment analysis, illustrate their practical utility. The video also introduces Google's generative AI development tools, including AI Studio, Vertex AI, and Model Garden, highlighting their role in enhancing LLM capabilities and accessibility.

Takeaways

• 🧠 Large Language Models (LLMs) are a subset of deep learning designed for pre-training on vast datasets and fine-tuning for specific tasks.
• 🐶 The concept of pre-training and fine-tuning in LLMs is likened to training a dog with basic commands and then adding specialized training for specific roles.
• 📚 LLMs are characterized by their large size, referring to both the extensive training data and the high number of parameters in the model.
• 🔧 LLMs are 'general purpose', meaning they are trained to handle common language tasks across various industries before being fine-tuned for specific applications.
• 🔑 The benefits of LLMs include their versatility in handling multiple tasks, minimal requirement for field training data, and continuous performance improvement with more data and parameters.
• 🌐 Generative AI, which includes LLMs, can produce new content like text, images, audio, and synthetic data, extending beyond traditional programming and neural networks.
• 📈 Google's release of Palm, a 540 billion parameter model, demonstrates the state-of-the-art performance in language tasks and the efficiency of the Pathways AI architecture.
• 🤖 The Transformer model, which includes an encoder and decoder, is fundamental to how LLMs process input sequences and generate relevant tasks.
• 🛠️ LLM development contrasts with traditional machine learning, requiring less expertise and training data, focusing instead on prompt design for natural language processing.
• 📊 Prompt design and engineering are crucial for optimizing LLM performance, with differences between creating task-specific prompts and improving system accuracy.
• 🔄 There are three types of LLMs: generic, instruction-tuned, and dialogue-tuned, each requiring different prompting strategies to achieve optimal results.
• 🔧 Google Cloud provides tools like Vertex AI, AI Platform, and generative AI Studio to help developers leverage and customize LLMs for various applications.

Q & A

What are large language models (LLMs)?

Large language models (LLMs) are a subset of deep learning that refers to large general-purpose language models that can be pre-trained and then fine-tuned for specific purposes.

How do large language models intersect with generative AI?

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

What does 'pre-trained' and 'fine-tuned' mean in the context of large language models?

Pre-trained means that the model is initially trained for general purposes to solve common language problems. Fine-tuned refers to the process of tailoring the model to solve specific problems in different fields using a smaller dataset.

What are the two meanings indicated by the word 'large' in the context of LLMs?

The word 'large' indicates the enormous size of the training dataset and the high parameter count in machine learning, which are the memories and knowledge the machine learned from the model training.

Why are large language models considered 'general purpose'?

Large language models are considered 'general purpose' because they are trained to solve common language problems across various industries, making them versatile for different tasks.

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 for specific problems, and continuous performance improvement as more data and parameters are added.

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

An example is Palm, a 540 billion parameter model released by Google in April 2022, which achieves state-of-the-art performance across multiple language tasks.

What is a Transformer model in the context of LLMs?

A Transformer model is a type of neural network architecture that consists of an encoder and a decoder. The encoder encodes the input sequence and passes it to the decoder, which learns to decode the representations for a relevant task.

What is the difference between prompt design and prompt engineering in natural language processing?

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

How does the development process of LLMs differ from traditional machine learning development?

LLM development requires thinking about prompt design rather than needing expertise, training examples, compute time, and hardware, making it more accessible for non-experts.

What are the three types of large language models mentioned in the script and how do they differ?

The three types are generic language models, instruction-tuned models, and dialogue-tuned models. Generic models predict the next word based on training data, instruction-tuned models predict responses to given instructions, and dialogue-tuned models are trained for conversational interactions.

What is Chain of Thought reasoning and why is it important for models?

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, which helps in improving the accuracy of the model's responses.

What are Parameter Efficient Tuning Methods (PETM) and how do they benefit LLMs?

PETM are methods for tuning a large language model on custom data without altering the base model. Instead, a small number of add-on layers are tuned, which can be swapped in and out at inference time, making the tuning process more efficient.

Can you explain the role of generative AI Studio in developing LLMs?

Generative AI Studio allows developers to quickly explore and customize generative AI models for their applications on Google Cloud, providing tools and resources to facilitate the creation and deployment of these models.

What is Vertex AI and how can it assist in building AI applications?

Vertex AI is a tool that helps developers build generative AI search and conversation applications for customers and employees with little or no coding and no prior machine learning experience.

What are the capabilities of the multimodal AI model Gemini?

Gemini is a multimodal AI model capable of analyzing images, understanding audio nuances, and interpreting programming code, allowing it to perform complex tasks that were previously impossible for AI.

Outlines

00:00

🤖 Introduction to Large Language Models (LLMs)

This paragraph introduces the concept of Large Language Models (LLMs), a subset of deep learning, and their intersection with generative AI. The speaker, a custom engineer at Google Cloud, aims to teach the audience everything about LLMs, including their definition, use cases, prompt tuning, and Google's AI development tools. LLMs are described as pre-trained, general-purpose language models that can be fine-tuned for specific tasks using smaller field data sets. The analogy of training a dog for everyday commands versus special service dog training illustrates the concept of pre-training and fine-tuning. The paragraph also touches on the three major features of LLMs: their large size in terms of training data and parameters, their general-purpose nature due to commonality in human language and resource restrictions, and the process of pre-training and fine-tuning.

05:02

🔍 Benefits and Features of Large Language Models

The benefits of using LLMs are outlined, emphasizing their versatility for various tasks, minimal requirement for field training data, and continuous performance improvement. The example of Google's 540 billion parameter model, Palm, showcases state-of-the-art performance in multiple language tasks. Palm is a dense decoder-only Transformer model that leverages the new Pathways system for efficient training across multiple TPU V4 pods. The paragraph further explains the concept of Transformer models, which consist of an encoder and a decoder, and how they have evolved from traditional programming to neural networks and generative models. The speaker also compares LLM development with traditional machine learning development, highlighting the ease of use and reduced requirements for expertise and training examples in LLM development.

10:04

📝 Use Cases and Prompt Design for LLMs

This section delves into the use cases of LLMs, particularly in question answering (QA), a subfield of natural language processing. It demonstrates how QA systems can answer a wide range of questions with minimal domain knowledge required due to the generative nature of LLMs. The speaker provides examples of a large language model chatbot, Gemini, answering various questions, illustrating the importance of prompt design and engineering in achieving accurate responses. The paragraph distinguishes between generic language models, instruction-tuned models, and dialogue-tuned models, each requiring different prompting strategies. It also introduces the concept of Chain of Thought reasoning, where models are more likely to provide correct answers when they first output explanatory text.

15:05

🛠️ Tuning and Tools for Enhancing LLMs

The final paragraph discusses methods for tuning LLMs to make them more reliable for specific tasks, such as sentiment analysis or occupancy analytics. It explains the process of adapting a model to a new domain by training it on new data, and the more resource-intensive process of fine-tuning, which involves retraining the model with a custom dataset. The paragraph introduces parameter-efficient tuning methods that allow for model tuning without altering the base model. It also highlights Google Cloud's offerings for enhancing LLMs, including Generative AI Studio for exploring and customizing models, Vertex AI for building AI applications without coding experience, and the Model Garden for continuous updates on new models. The speaker concludes by emphasizing the adaptability and scalability of Gemini, a multimodal AI model capable of analyzing various data types, and invites the audience to explore further AI topics in other videos.

Keywords

💡Large Language Models (LLMs)

Large language models are advanced deep learning models designed to process and generate human-like text. In the video, LLMs are described as pre-trained models that can be fine-tuned for specific purposes, such as text classification, question answering, and document summarization. They are central to the theme of the video, which explains their capabilities and applications.

💡Generative AI

Generative AI refers to artificial intelligence that can create new content, including text, images, audio, and synthetic data. The video highlights how generative AI intersects with LLMs, emphasizing their ability to produce diverse outputs based on vast amounts of training data. This concept is foundational to understanding the potential and versatility of LLMs.

💡Pre-trained Models

Pre-trained models are those that have been initially trained on large datasets for general purposes before being fine-tuned for specific tasks. The video uses the analogy of training a dog to explain how pre-trained models can handle common language problems and later be adapted to specialized fields, illustrating the efficiency and scalability of LLMs.

💡Fine-tuning

Fine-tuning is the process of adapting a pre-trained model to perform specific tasks by training it on smaller, task-specific datasets. In the video, fine-tuning is compared to providing specialized training to a service dog, highlighting its role in customizing LLMs for various applications, such as retail, finance, and entertainment.

💡Transformer Model

A transformer model is a type of deep learning model used for processing sequences of data, such as text. The video explains that transformers consist of an encoder and a decoder, which work together to understand and generate language. This architecture underpins many LLMs, including Google's Pathways Language Model (PaLM).

💡Prompt Design

Prompt design involves creating clear, concise, and informative prompts to guide the outputs of language models. The video emphasizes the importance of prompt design in achieving accurate and relevant results from LLMs, especially in tasks like question answering and text generation. It is a crucial aspect of natural language processing.

💡Parameter Count

Parameter count refers to the number of parameters, or learnable components, in a machine learning model. The video explains that large language models have billions of parameters, which contribute to their ability to handle complex language tasks. This concept is linked to the 'large' in LLMs, indicating their extensive capacity for learning.

💡Zero-shot Learning

Zero-shot learning is a scenario where a model can recognize and perform tasks it was not explicitly trained on. The video discusses how LLMs can handle zero-shot learning, meaning they can generate accurate responses to new tasks without additional training, demonstrating the versatility and robustness of these models.

💡Generative AI Studio

Generative AI Studio is a tool provided by Google Cloud to help developers explore and customize generative AI models. The video describes it as part of the resources available for leveraging LLMs, highlighting its role in making AI more accessible and easier to integrate into applications. This tool supports the theme of democratizing AI technology.

💡Pathways Language Model (PaLM)

Pathways Language Model (PaLM) is a state-of-the-art LLM developed by Google, featuring 540 billion parameters. The video mentions PaLM as an example of a highly advanced model that achieves superior performance across multiple language tasks. It illustrates the cutting-edge capabilities of LLMs and their continuous improvement with more data and parameters.

Highlights

Introduction to Large Language Models (LLMs) and their significance in the field of AI.

LLMs are a subset of deep learning, intersecting with generative AI to produce new content types.

Definition of LLMs as large, general-purpose language models that are pre-trained and fine-tuned for specific tasks.

Explanation of the terms 'pre-trained' and 'fine-tuned' using the analogy of training a dog for special service.

Major features of LLMs including the size of training data sets and parameter count in machine learning.

The concept of 'general purpose' in LLMs and its relation to common language tasks and resource restrictions.

Benefits of using LLMs such as versatility across different tasks and minimal field training data requirements.

Performance improvement of LLMs with the addition of more data and parameters, exemplified by Google's release of Palm.

Description of Palm as a 540 billion parameter model leveraging the new pathway system for efficient training.

Introduction to the Transformer model architecture consisting of an encoder and a decoder in LLMs.

Comparison between traditional programming, neural networks, and the generative capabilities of LLMs.

Demonstration of LLMs' ability to generate content through examples of question answering and text generation.

Differences between LLM development and traditional machine learning development in terms of expertise and requirements.

Examples of how prompt design and prompt engineering improve the performance of LLMs in natural language processing.

Types of LLMs: generic, instruction-tuned, and dialogue-tuned, and their specific prompting needs.

Importance of Chain of Thought reasoning in improving the accuracy of LLMs' responses.

Practical limitations of LLMs and how task-specific tuning can enhance their reliability.

Google Cloud's tools for enhancing LLMs, including Generative AI Studio, Vertex AI, and Model Garden.

Introduction to Gemini, a multimodal AI model capable of analyzing images, audio, and programming code.

Conclusion summarizing the knowledge imparted about LLMs and their practical applications in AI.

Transcripts

[Music]

how's it going I'm M today I'm going to

be talking about large language models

don't know what those are me either just

kidding I actually know what I'm talking

about I'm a custom engineer here at

Google cloud and today I'm going to

teach you everything you need to know

about llms that's short for large

language models in this course you're

going to learn to Define large language

models describe llm use cases explain

prompt tuning and describe Google's

generative AI development tools let's

get into it large language models or

llms are a subset of deep learning to

find out more about deep learning check

out 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 all right back to llms 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 great questions

let's dive in 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 Canan citizen good boy

but if you need special service dogs

such as a police dog a guide dog or a

hunting dog you add special trainings

right 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

so now that you've got that down let's

further break down the concept into

three major features of large language

models we'll start with the word large

large indicates two meanings first is

the enormous size of the training data

set sometimes at the pedy scale second

it refers to the parameter count in

machine learning parameters are often

called hyperparameters parameters are

basically the memories and the knowledge

the machine learned from the model

training parameters Define the skill of

model in solving a problem such as

predicting text so that's why we use the

word large what about general purpose

general purpose is when the models are

sufficient to solve common problems two

reasons led to this idea first is the

commonality of 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 so this leaves us with our

last terms pre-trained and fine-tuned

which mean to pre-train a large language

model for a general purpose with a large

data set and then find tune it with

specific aims with a much smaller data

set so now that we've nailed down the

definition of what large language models

llms are we can move on to describing

llm use cases 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 pedabytes

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 f shot or even zero shot

scenarios in machine learning f 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 pump short for a

Pathways language model a 540 billion

parameter model model that achieves a

state-of-the-art performance across

multiple language tasks pal is a dense

decoder only Transformer model it

leverages the new pathway system which

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 but I'm get

ahead of myself I previously mentioned

that pal is a Transformer model let me

explain what that means a Transformer

model consists of 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 we've come a long

way from traditional programming to

neuron networks to generative models in

traditional programming we used to have

to hardcode the rules for distinguishing

a cat type animal legs four ears to fur

yes likes yarn and catnip in the wave of

neural networks we could give the

network pictures of cats and dogs and

ask is this a cat and they would predict

a cat what's really cool is that in the

generative wave we as users can generate

our own content whether it be text

images audio video or more for example

models like pal or Pathways language

model or Lambda language model for

dialogue applications ingest very very

large data from multiple sources across

the internet and built Foundation

language models we can use simply by

asking a question whether typing it into

a prompt or verly talking into the

prompt itself 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 a process of creating a

prompt that is clear concise and

informative it is an important part of

natural language processing or NLP for

short in traditional machine learning

you need expertise training examples

compute time and Hardware that's a lot

more requirements than llm development

let's take a look at an example of a

text generation use case to really drive

the point home 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

needed domain knowledge to develop these

question answering models let's make

this clear with the real world example

domain knowledge is required to develop

a question answering model for customer

it support or healthare care or supply

chain but using generative QA the model

generates free text directly based on

the context there's no need for domain

knowledge let me show you a few more

examples of how cool this is let's look

at three questions given to Gemini a

large language model chatbot developed

by Google AI question one this year's

sales are

$100,000 expenses are

$60,000 how much is net profit Gemini

first shares how net profit is

calculated then performs the calculation

then Gemini 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 Gemini 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 Gemini answers the

question with an example on how to solve

the problem and some additional context

so how is that in each of our questions

a desired response was obtained this is

due to prompt design fancy 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 but 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 the system is being asked

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 a 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 in

general 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 let's start with generic

language models generic language models

predict the next word based on the

language in the training data here is a

generic language model in this example

the cat sat on the next word should Beth

and you can see thatth is most likely

the next word think of this model type

as an autocomplete in search next we

have instruction tuned models this type

of 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 in this

example classify text into neutral

negative or positive and finally we have

dialog tuned models this model is

trained to have a dialogue by the next

response dialogue tune 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 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

but there is a catch there's always a

catch a model that can do everything has

practical

limitations but task specific tuning can

make llms more reliable vertex AI

provides task specific Foundation models

let's get into how you can tune with

some real world examples let's say you

have a use case where you need to gather

how your customers are feeling about

your product or service you can use a

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

tasks that you want the model to perform

it is essentially the process of

adapting a model to a new domain or a

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 Healthcare data the tasks

include question answering image

analysis finding similar patients Etc

fine tuning is expensive and not

realistic in many cases so are there

more efficient methods of tuning yes

parameter efficient tuning methods 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 I'm going to tell you about three

other ways Google Cloud can help you get

more out of your llms the first is

generative a studio 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 is 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 next we have verx ai which

is particularly helpful for those you

who don't have much coding experience

you can build generative AI search and

conversations for customers and

employees with verx AI search and

conversation formerly gen app builder

build with little or no coding and no

prior machine learning experience RX AI

can help you create your own chat Bots

digital assistants custom search engines

knowledge bases training applications

and more and lastly we have pom APR pom

APR lets you test and experiment with

Google's large language models and J

tools to make prototyping quick and more

accessible developers can integrate

palom 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 and what do these tools

do I'm so glad you asked the model

training tool helps developers train

machine learning models on their dat

data using different algorithms the

model deployment tool helps developers

deploy machine learning models to

production with a number of different

deployment options the model monitoring

tool helps developers monitor the

performance of their machine learning

models in production using a dashboard

and a number of different

metrics Gemini is a multimodal AI model

unlike traditional language models it's

not limited to understanding text alone

it can analyze images understand the

nuances of audio and even interpret

programming code this allows Gemini to

perform complex tasks that were

previously impossible for AI due to its

Advanced architecture Gemini is

incredibly adaptable and scalable making

it suitable for diverse applications

model Garden is continuously updated to

include new models see I told you way

back in the beginning of this video that

I knew what I was talking about when it

came to large language models and now

you do too thank you for watching our

course and make sure check out our other

videos if you want to learn more about

how you can use

[Music]

AI