Introduction to Large Language Models
Summary
TLDRThis 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
🤖 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.
🔍 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.
📝 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.
🛠️ 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.
Mindmap
Keywords
💡Large Language Models (LLMs)
💡Generative AI
💡Pre-trained Models
💡Fine-tuning
💡Transformer Model
💡Prompt Design
💡Parameter Count
💡Zero-shot Learning
💡Generative AI Studio
💡Pathways Language Model (PaLM)
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
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