A basic introduction to LLM | Ideas behind ChatGPT

Summary

The video discusses language models and large language models like GPT and ChatGPT. It explains how LMs work by predicting the next word in a sequence to model patterns in human language. As more training data and parameters are added, LMs become LLMs like GPT and can be used in solutions for tasks like question answering. The video also introduces concepts like prompt engineering, model security, giving LMs access to tools through APIs, reasoning in LMs, retrieval augmented generation, and model fine-tuning.

Takeaways

• 😀 Language models (LMs) predict the next word in a sequence based on patterns in training data
• 📚 LMs can be used to build solutions like question answering systems
• 🔬 Researchers use more data and parameters to create large LMs (LLMs)
• 💰 LLMs require lots of compute and are expensive to train
• 🤗 Some LLMs are open source and can run locally without APIs
• ✏️ Prompt engineering involves carefully crafting inputs to get desired LLM outputs
• 🔒 There are security concerns around malicious use of powerful LLMs
• ⚙️ LLMs can be given access to tools through APIs to take actions
• 🧠 Making LLMs exhibit reasoning is an area of research
• 📝 Fine-tuning trains parts of a model for specialized tasks

Q & A

What is a language model and how does it work?

A language model (LM) takes a sequence of words as input and predicts the next word. It tries to model the patterns in human language based on the data it has been trained on.

How can language models be useful?

Instead of giving an LM random sentences, we can give it questions and instructions to get useful outputs like answers. With enough data and model capacity, LMs can be used to build solutions.

Why do large language models require so much data and compute?

To model the complexity of human language, LMs need to be trained on internet-scale data (10s of TBs). Bigger models with 100s of billions of parameters also require specialized GPUs for training over months.

What are some popular large language models?

GPT by OpenAI, LLama by Meta, Falcon by Microsoft/FAIR, Bloom by Anthropic, and more. Many are now open source so you can run them locally.

What is prompt engineering for large language models?

The way inputs are formatted and fed to LMs can greatly impact outputs. Prompt engineering studies how to frame prompts to get desired and accurate responses from LMs.

How can LMs access tools through APIs?

LMs can be instructed to output API calls instead of just text. These payloads can then be used to actually invoke those APIs and take actions.

What security concerns exist around large language models?

Potential issues include generating harmful text, prompt hacking to force unsafe outputs, and more. Work is being done to make LMs secure.

What does retrieval augmented generation mean?

When an LM needs extra context documents to answer questions, relevant chunks can be retrieved and added to the prompt for better responses.

How does fine-tuning a large language model work?

Task-specific layers can be added and trained on top of a pre-trained LM for customized performance on specialized datasets.

What other focus areas exist for improving large language models?

Giving LMs reasoning abilities, tools access through APIs, prompt engineering for better responses, and security.

Outlines

00:00

🤓 What is a language model

A language model (LM) takes a sequence of words as input and predicts the next word based on patterns it has learned from training data. LMs are useful for building solutions like question answering by formatting prompts in certain ways. The more data used to train LMs, the better they get.

05:02

🌟Scaling up language models into LLMs

To improve language models, researchers use internet-scale data and increase model sizes into the billions of parameters. This requires a massive amount of compute and funding, resulting in large language models (LLMs) that only big organizations can train over months. Some LLMs are now open source.

10:02

🎯 Using and customizing LLMs

Pre-trained LLMs like LLaMA can be downloaded and run locally. Prompt engineering refers to formatting prompts to LLMs in ways that produce better, more accurate responses. Fine-tuning allows customizing LLMs for specific tasks by re-training only certain model layers.

15:04

🔎 Other areas around LLMs

Some other active areas around LLMs include retrieval augmented generation (RAG) for providing documents as context, ACT which gives LLMs access to take actions, security to prevent illicit content generation, jailbreaking LLMs, and trying to add reasoning and thinking capabilities.

Keywords

💡Language Model (LM)

A Language Model (LM) is a computational algorithm designed to understand, interpret, and generate human language based on the pattern of words. In the video, it's described as a system that takes a string of text as input, like 'she is,' and predicts the next word, such as 'watching,' based on the data it has been trained on. The LM's ability to predict subsequent words showcases its fundamental role in processing and generating human-like text, making it central to the development of products like ChatGPT and other language-based applications.

💡Large Language Models (LLMs)

Large Language Models (LLMs) are advanced versions of language models that have been trained on extensive datasets, often spanning billions of parameters and requiring significant computational resources. The video emphasizes that LLMs, like GPT and its variations, are powered by huge, diverse datasets and state-of-the-art architectures like Transformers. They represent the cutting edge in the field, capable of understanding and generating text with a high degree of complexity and nuance.

💡Transformers

Transformers are a type of neural network architecture that has significantly advanced the capabilities of language models. They are highlighted in the video as the driving force behind the current state-of-the-art language models, including GPT. Transformers excel at processing sequences of data, such as text, making them particularly effective for tasks involving natural language understanding and generation.

💡Prompt Engineering

Prompt Engineering is a technique discussed in the video that involves carefully crafting the input given to a language model to elicit a desired output. It underscores the strategic manipulation of prompts to improve the accuracy and relevance of responses from LMs, highlighting how users can guide the model's behavior to achieve specific objectives, such as sentiment analysis or answering questions accurately.

💡Fine-tuning

Fine-tuning is a process of adjusting a pre-trained model on a smaller, specialized dataset to tailor it to specific tasks or improve its performance on particular types of data. The video discusses how fine-tuning can adapt a general-purpose LM, like GPT, to perform specialized tasks by training it further on a dataset with specific input-output pairs, thereby enhancing its utility for customized applications.

💡API

API, or Application Programming Interface, is mentioned in the context of enabling language models to perform actions, such as booking a flight, by making API calls to external services. This concept illustrates the potential of LMs to not just generate text, but to interact with other software systems, thereby executing tasks or retrieving information from the web or specialized databases.

💡Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) is a technique where a language model enhances its responses by incorporating external information. In the video, RAG is described as augmenting the generation process by retrieving relevant chunks of information from a larger document or dataset to provide context or specific answers that wouldn't be possible based solely on the model's pre-existing knowledge.

💡Security in LLMs

The video touches on the importance of ensuring security in language models, specifically in preventing them from generating harmful, biased, or inappropriate content. It mentions mechanisms to restrict LMs from responding to certain queries or prompt hacking, which involves trying to bypass these restrictions. This concept underscores the ethical considerations and technical challenges in developing and deploying LLMs.

💡Open Source LLMs

Open Source LLMs are freely available models that can be used, modified, and distributed by anyone. The video highlights how organizations like Meta have released models like LLaMA to the public, democratizing access to state-of-the-art language technologies. This practice allows a wider community to build upon these advancements, fostering innovation and accessibility in the field.

💡Compute Resources

Compute resources, particularly GPUs (Graphics Processing Units), are essential for training large language models due to their intensive computational demands. The video discusses how the scale of LLMs requires substantial investment in high-performance computing infrastructure, making the development of these models feasible primarily for well-funded organizations. This point highlights the technological and financial barriers to entry in the field of AI research and development.

Highlights

Language models predict the next word in a sequence based on patterns in training data

LMs can be used to build solutions by prompting them with questions and getting back answers

Researchers increase training data and model parameters to improve LMs

Large LMs require massive compute and are expensive to train

Some large LMs are open sourced for anyone to use

Prompt engineering tunes inputs to get better LM outputs

Giving LMs access to tools enables them to take actions beyond just answering

Security is needed to prevent harmful LM responses

LMs currently lack reasoning and a thought process

Retrieval augmentation retrieves relevant context to answer questions

Fine tuning trains parts of a model for specialized tasks

RAG uses retrieval to provide context for generation

Fine tuning controls how LMs generate text

Pre-trained LMs can be fine-tuned for specific projects

Fine tuning is efficient as only parts of LM are retrained

Transcripts

hello everyone this is Yash and I'll be

talking about language models and large

language models some of the ideas that

encapsulate uh Chad GPD and uh other

similar products it's very hot these

days these products and uh I just want

to discuss the ideas or or the story

line uh that comes uh along with this

products uh we'll not be implementing or

coding anything in this video but maybe

in the in the next or the subsequent

videos we'll actually get get get our

hands dirty and we'll build something as

well so uh the thing that the central

idea around all of this is uh a language

model or an LM so what exactly is an LM

an LM takes a string as input so let's

say an example like she is and uh it

will sort of predict the next word so

she is uh let's say uh watching so let's

say this is what uh the LM has predicted

watching now uh there can be many uh

words that can come instead of watching

but it just uh predicts like the top

probable topmost probable word it highly

depends on the kind of data that rlm has

been trained

on uh you see it it it's not uh it's

like human language it's not very random

like the order of words it's not

extremely random there is some pattern

to it and uh the LM exactly tries to uh

model that

pattern uh it's like U she is uh like

running sleeping you can say these kind

of words uh maybe like nouns as well

like she's president or or some

adjective like she's beautiful or

something and uh but there won't be like

something like is again so let's say she

is is so is as a prediction is quite uh

less likely or or very rare uh you can

say it's almost

impossible uh or words like January so

uh so she is January uh January is also

possible so she is January born or

something uh we can say like in this

case it becomes a adverb of time January

so uh so it's it's it's possible uh but

it's still uh you know like uh very rare

Maybe uh or or or less likely not very

rare but uh less likely compared to

watching or or some verb like sleeping

or

something so this is how the LMS work it

highly depends on the data that it has

been trained on the data that is needed

for an LM to

train it's uh it's just a plain Text

data so uh so just the text uh like con

a book or something which has a human

language like a proper human

language uh which is readable and

understandable it's not like completely

random or anything so if you give that

to an LM model it will uh sort of model

uh the patterns that are there it'll try

to learn it the more the data the better

it is for the model uh if you want to

see an example of how an LM Works

actually and the some little bit of math

behind it I've made a video around

engram language model it's a very simple

uh model how we can uh build it uh there

are more sophisticated ones after that

which uh which uses neural network deep

learning and now uh there's a whole uh

bunch of models which uses

Transformers so Transformers have been

shown to perform the best uh and uh that

is what is driving the current State

ofthe art and chat GPD and all of those

things now if you if you think about it

uh if you really think about it uh

you'll ask uh how are these LMS even

useful right we are giving in some uh

input and uh we are getting the

completion basically like like the words

how is it even useful so uh what how we

can think about it is U we can start

using these LMS to actually build

Solutions so what do I mean is U rather

than uh you know giving it a random

sentence we can actually give it a like

a question so let's say I give a

question like

capital

of India so uh and and what I can do is

I can write answer and I can end the

string there so if I say if this is my

string qu I I I I write a q q for

question and I end it with an A so

hopefully it'll complete it with the

answer right so this is the idea like

we'll start getting answers we can start

getting answers as well but uh it really

depends on the data that LM has been

trained on if uh LM has never seen Delhi

or something in its text this might be

difficult uh to uh still give give the

solution for so this is how the story

continues and uh that is what

researchers uh uh do so what they do is

uh they they sort of

increase the

data uh which is used to train uh these

language models and uh when the data

goes up uh we uh increase the model size

as

well uh what do I mean by increase the

model size is we increase the learnable

parameters in in in the model so more

parameters can be learned or or more

weights are there and uh increase of

data means internet scale of data which

includes uh like a uh web crawl or or

you can say like a chunk of Internet

which uh whichever is possible like

publicly available and which can be

which can be crawled or scraped so for

instance uh this video I'm putting uh

maybe like the transcript of this video

can be taken and uh you'll get that text

to uh you know train your uh language

model uh the posts Maybe you uh might

have made some Facebook post or Reddit

post or something so those posts can

also be included in in within the data

so it's all on the internet people like

you have me have contributed all this

data over the past years and this data

can be used to train the language models

now when the data increases uh maybe in

the internet scale so it's like around

10 TBS of data that we are looking at

and uh the model parameter are in

billions so it's like tens or even

hundreds of billions of parameters are

present so these are like tens or even

hundreds of billions of

parameters uh we'll also need uh so uh

if if the model goes this big and if we

are training this large of data we will

also need more

compute very high compute and by compute

I mean

GPU so uh so that's where the whole

story comes to an

end uh we'll require a lot of GPU which

requires a lot of money so with this

money goes up uh and that's why all the

big organizations are able to train uh

these big

models and uh when you sort of train

these language models on this huge of of

of data which is actually so it's so big

then it's called as llm which is large

language model and uh all these big

organizations can do it because it costs

millions and it requires some time for

uh for it to be trained maybe like

months or so uh with a lot of gpus uh so

they can afford it so they train it and

uh yeah we don't do anything we only use

it I guess uh but yeah like uh some

organizations have been uh very kind now

to open source their models as well so

these llms um it's available the the

pre-trained llms so you don't have to do

do any training you can just pick the

model and run it and it runs on uh on

your local machine as well so there are

these uh llms that uh that come in

market right so everyone knows GPT or uh

GPT is the LM and uh Chad GPT is like

the product or or you can say like a

fine tune version of it uh this this

small model is proprietary it's not you

can access it only through API or U web

interface uh or or like the web browser

that you use right to use chat GPT or

something but you can't run GPT on your

local machine unless you work for open a

but uh some some companies some

organizations uh like meta has uh

launched llama uh it's a llama 2 or

llama or or you can say llama series of

models or uh there is other models like

Falcon and uh Bloom and other models are

there like lot lots of models are there

maybe depending on the time when you're

watching this videos there will be many

more new better models that come out

hopefully so uh for now these are the

this is the state and uh these models

you can actually download them and run

run it on your local you don't have to

use any internet to use them I mean once

you download it uh you you can just run

it and without any API or without any

web interface or anything as such you

can just use these models out of the box

we'll see how how to exactly use these

models in the next video but uh that's

uh about it that's like the whole idea

of U how the llm story comes into

picture uh there is uh lots of things

like around llm so I would like I I just

want to talk about prompt engineering as

a field that came up so uh when when we

give some input sentence or some

instructions uh to these

llms there are specific way in which uh

we can specify these inputs to llm so

that we get desired output or desired

answers so let's say sentiment uh

sentiment classification if I say if I'm

um asking the llm to do to tell me like

what sentiment this sentence belongs to

and uh I'm just passing it uh I'm just

passing in the

sentence uh so maybe rather than me

saying it that way it it makes more

sense to put the sentence first and then

say like what is the above sentence

sentiment for the above sentence

something like that so so the the way

you uh put in this input uh is called

prompt engineering and researchers have

found that uh if you uh for for certain

questions if you do prompting in certain

way uh it will give uh more accurate

answers so we'll see all that it's very

interesting uh the other part is uh act

which comes uh around U uh like when we

talk about llms uh these things uh come

around and uh what what do I mean by Act

is we get give llms access to tools so

we give some tools to these llms which

uh llms can access while apis what do I

mean by that is when we prompt something

like uh let's say book of flight so we

don't just want llm to uh you know tell

us something but we actually want LM to

do something so rather than just uh you

know uh printing the answer or something

it can print like API calls so API call

and uh it might say the source and

destination and it will uh oh I I I

think I should draw it here so it it

might say like source and destination

and and it can send the

API uh to some endpoint so as soon as we

see this text appearing API call we can

actually copy paste this payload and we

can actually make the API call as well

so that is what um it means by giving

access to tools to these llms and uh

yeah that is the whole field of act so

other than act there is also this field

of uh security or around llms uh like

how can we uh make llms not to generate

any profane kind of words or wrong

things so uh something like if someone

asks how to destroy this

planet uh we don't want llms to answer

that maybe so how do we stop llms to do

that and um uh and uh even if we stop

there is a whole field of jailbreaking

and uh you know prompt hacking

which comes like even if it's not

answering how to try and still get the

answer out using these llms so those are

some some more things uh security

related things yeah and there is uh one

more part like uh thinking part so it

comes uh around llm again so llms right

now is just generating uh the completion

or the answer we can say but it's not

really thinking it it doesn't have any

you know sense of knowledge so or or or

or I I shouldn't say knowledge I should

say like uh the reasoning it it doesn't

have any reasoning as such so how can we

uh you know make LMS U make like a

thinking tree that uh hey it's because

of this word that is present in the

sentence that is why the sentiment can

be like this or or it it should be able

to sort of reason these things and uh

you know like the whole thought process

should go on within lm's mind so this is

another field around llm that is there

uh these are some of the things that are

around llm prompt engineering security

acting thinking uh there can be many

more I know only these uh if you know

some you can put in the comments as well

uh so yeah in the next video we'll

actually see uh you know maybe we can

start with U llama or something and uh

how we can actually use uh llama models

or or some existing open source

uh llms um to get these answers and

all uh so yeah thanks for watching uh if

you have any queries you can put in the

comments or uh we can also connect on

LinkedIn uh I'll give the link in the

description if you want uh okay I'm

sorry uh there are two things I I also

wanted to uh you know I thought I'll say

it in in next videos but uh this seems

like a right place so I'll say it here

itself so there are also these concepts

of uh rag and uh fine tuning so they

also come along uh when we talk about

llms uh so along with prompt engineering

and uh security thinking acting uh we

also have these two things a rag uh this

is retrieval augmented generation so uh

let's say you want to you have a

document uh like like an internal

document uh which uh llm doesn't know

about

uh so uh like if you want to pass it

through llm this is the doc and uh if

you want to sort of uh you know talk to

this dog or do question answering from

this dog if the answer is contained

within this dog then uh llm might not be

able to answer it purely without any

context so what we do is we uh we

augment this document along with our

question and we say that uh hey can you

find the answer from from this document

and all the context is attached with it

now uh llms have like a fixed window of

input it can't take uh infinite input so

very long documents uh can't be

processed through llm so what we do is

we break the these documents uh into

chunks and one of the chunk is retrieved

where the answer is present and that

chunk is added as context while

prompting the llm for answer so this is

uh like we retrieve the most relevant

Chunk from this document and then we ask

the question so that the answer is

present in that context itself and a

whole document need not be provided to

the llm so that is what we call

retrieval augmented

generation uh we augment the generation

process the answer generation process

with uh this retrieval step we'll see

how it works and uh I'm planning to make

more videos on this in detail but uh I

thought I just at least introduce this

concept here in this video uh then we

have finetuning so uh let's say you have

like a particular data set uh with like

X and Y so okay this is X this is

y uh so you have uh like a proper input

output kind of pairs uh like a very

specific task for your use case your uh

maybe you're working on some project or

or as an organization you have some

project where uh you want to do very

very specific spe ific classification

kind of thing so maybe you have two

labels uh like like two intents or

something which is specific to your

project uh so llms might not be uh you

know like out of the box it might not be

able to classify them but uh if we have

the data set for those we can uh

finetune an

llm we can fine tune an llm and

uh uh then we can use the llm by giving

X as input and uh we hopefully get y as

output uh we'll see how everything works

uh in fine tuning there are there are a

lot of things in fine tuning it's not

just about X and Y pairs uh it's about

the way llm speaks as well uh like we

can sort of guide the llm to speak or or

to generate in a certain way uh in a

certain style or in certain formats so

that is where fine tuning uh becomes

very useful uh these these models that

that we spoke about llama Falcon they

are not fine-tuned models they are

pre-trained models and that those models

we use them as base base models and we

sort of U if this is the base model

let's say this llm then we add like a

small layer which we train so we need

not train the whole model we just do the

training of this layer or some or it's

called

fine-tuning Uh only only specific parts

of the model are trained so that it does

is it's not very computationally

expensive uh we'll see how how this is

done as well in subsequent videos I'm

planning to make this uh like way ahead

in future maybe not not the next video

right now but uh this is also very

interesting and uh we'll see how llm

generates with fine tuning as well yeah

that's all thanks