Large Language Models:
Challenges and Opportunities
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Evolution of How Information is Stored and Retrieved !
Stone/Iron Age
Industrial Age
Digital Age
Carved in Stones
Written on papers
Digitized
Parameterized
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The Age of AI [has begun]*
Store and Retrieve
Store and Retrieve
Store and Retrieve
Store and Generate!
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Magic Box
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Creative Text Generation
"Any sufficiently Advanced Technology is Indistinguishable from Magic"
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Simple Sentiment Classification
Magic Box
"Any sufficiently Advanced Technology is Indistinguishable from Magic"
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Logical Reasoning
Magic Box
"Any sufficiently Advanced Technology is Indistinguishable from Magic"
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Doing arithmetic
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Magic Box
"Any sufficiently Advanced Technology is Indistinguishable from Magic"
Who is inside the Magic box?
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I'm sure there must be a few expert dwarves in the box!
That's why we get convincing responses for all questions
Who is inside the Magic box?
Well, It generates an Image from a textual description!
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Image: Children suspects some people are inside a radio or television set back in 1970's, India.
There must be a dwarf inside the box..
Who is inside the Magic box?
Image: Children suspects some people are inside a radio or television set back in 1970's, India.
Well, It generates an Image from a textual description!
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There must be a dwarf inside the box..
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Magic Box
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"Any sufficiently Advanced Technology is indistinguishable from Magic"
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Magic Box
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Multi-head Masked Attention
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tell
me
a
joke
about
idli
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why
why
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did
the
did
Multi-head Masked Attention
tell
me
a
joke
about
idli
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why
why
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did
the
did
The Magic:
Train the models to predict next word given all previous words
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idli
the
“The magician takes the ordinary something and makes it do something extraordinary.”
Traditional NLP Models
Large Language Models
Input text
Predict the class/sentiment
Input text
Summarize
Question
Answer
Input text
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LLMs
Prompt: Input text
Output response conditioned on prompt
Prompt: Predict sentiment, summarize, fill in the blank, generate story
Labelled data for task-1
Labelled data for task-2
Labelled data for task-3
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Raw text data
(cleaned)
Model-1
Model-2
Model-3
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Trillions of
Tokens
Billions of
Parameters
Zetta FLOPS
of Compute
LLMs
Three Stages
Pre-training
Fine tuning
Inference
Trident of LLMs
Trillions of Tokens
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LLMs
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Next token
“The magician takes the ordinary something and makes it do something extraordinary.”
Something Ordinary:
To Extraordinary:
Predict next token
and next token, next token, .........
Sourcing billions of tokens from the Internet is a massive engineering effort!!
Pre-Training
By doing this, the model eventually learns language structure, grammar and world knowledge !
Trillions of
Tokens
BookCorpus
Wikipedia
WebText(closed)
RealNews
The Pile
ROOTS
Falcon
RedPajama
DOLMA
C4
Opportunity:
Build one
Challenge:
Inadequate quality datasets for Indic Languages
C4
ROOTS
DOLMA
mC4
Sangraha
Dataset Name
# of tokens
~156 Billion
Diversity
Webpage
~170 Billion
22 sources
> 1 Trillion
380 Programing languages
5 Trillion (600B in public)
Webpage
1.2/30 Trillion
Webpage, Books, Arxiv, Wiki, StackExch
3 Trillion
Webpage, Books, Wiki, The Stack, STEM
~418 Billion
Webpage
~341 Billion
natural and programming languages
251 Billion
Web, videos, digitized pdf,synthetic
Languages
English
English
Code
English
English/Multi
English
Multi
Multi
Multi
Effort by AI4Bharat
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English data
Capture all India specific knowledge in all Indian Languages!
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Billions of
Parameters
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Fruit Fly
Honey Bee
Mouse
Cat
Brain
# Synapses
Transformer
GPT-2
Megatron LM
GPT-3
GShard
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Zetta FLOPS
of Compute
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Training LLMs having more than 70 Billion Parameters is affordable only for a few organizations around the world
Requires a cluster of A100 (or) H100 GPUs that requires millions of dollars
Then, how do we adapt those models for diverse Indian culture and languages
Way to go: Language Adaptation ?
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Trillions of
Tokens
Billions of
Parameters
Zetta FLOPS
of Compute
Pre-Trained open sourced LLM
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Way to go: Language Adaptation ?
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Trillions of
Tokens
Billions of
Parameters
Zetta FLOPS
of Compute
Pre-Trained open sourced LLM
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Billions of
Tokens
Billions of
Parameters
Peta FLOPS
of Compute
Fully fine-tuned
Sangraha
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Billions of
Parameters
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Fruit Fly
Honey Bee
Mouse
Cat
Brain
# Synapses
Transformer
GPT-2
Megatron LM
GPT-3
GShard
Affordable for inference
Opportunity:
Use Instruction Fine-tuning and build datasets for the same
Challenge:
(full) Fine-Tuning of LLMs on Indic datasets still requires a lot of compute and expensive
Way to go: Instruction Fine-Tuning?
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Billions of
Tokens
Billions of
Parameters
Peta FLOPS
of Compute
Fully fine-tuned
Sangraha
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LLMs exhibit a remarkable learning ability called "in context learning".
It means, we can instruct them to respond in certain way by giving them a set of examples about the task during inference [the cheapest option]
Way to go: Instruction Fine-Tuning
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Millions of
Tokens
Billions of
Parameters
Tera FLOPS
of Compute
Instruction-tuned
Indic-Align
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Goal:
Improve the model’s ability to understand and follow human instructions and ensure response is aligned with human expectations and values.
How it works:
Training the model on a set (relatively small) of high quality and diverse instruction and answer pairs.
How do we source the data?
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From all the places where a conversation happens!
Alpaca
Unnatural
Self-Instruct
Evolved Instruct
What should be the size?
Guanaco
Natural Inst
Ultra Chat
P3
FLAN
Significantly lesser
But more high quality!!
For Indian Languages ?
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Existing English Data
Synthetic India-centric conversations
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Indic-Align
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Capture all different ways in which people can ask!!
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Evaluation
How do we compare the performance of one model to the other?
How good is the model at solving a given task?
Are there any more hidden skills we dont know about?
Still a lot of open questions to explore here.
How good is the model in other languages?
Is the model biased? Is it Toxic? Is it Harmful?
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There are hundreds of models in the market (ChatGPT, Llama, Gemma, Sutra ..)
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What is the next big direction?
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If a single architecture works for Text, Image, sound and video, then why not train the architecture on all these modalities?
...and it is already happening
That's called multi-modal LLM
Again, What about the data for Indian Context?
It is both a challenge and an opportunity!
Opportunities are Plenty
We now know that
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Cost for Pre-training is prohibitive !
-
Cost for Fine-tuning is still expensive !
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but running the model in inference mode is cheaper
-
Requires far less computing than pre-training and fine-tuning
-
One can access via APIs [No need to setup anything]
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In inference mode, we can
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Tune a model to do a new task via In-Context Learning (few-shot prompting)
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Build apps that combine the power of LLMs with other tools to solve other problems!
We can build numerous applications !
Workshop Activity Details
Whats next?
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Building an end to end voice-enabled Chatbot
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Integrate the ASR, NMT and TTS models together with ChatGPT.
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RAG-based pipeline to build a Govt. Scheme bot.
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Help us build a real-world benchmark for evaluating LLMs in the Indian context and languages.
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Take home activity
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Just 25 prompts per person.
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Certificates for the workshop will be given to attendees upon successfully
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Completing the assigned 25 prompts
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Building your own voice-enabled chatbot.
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Image Credits
Opportunities-in-the-llm-field
By Arun Prakash
Opportunities-in-the-llm-field
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