Whitepaper Companion Podcast - Foundational LLMs & Text Generation

hey everyone and welcome back to the Deep dive today we're uh we're diving into the world of large language models llms llms yeah yeah you know those AI systems are making so much noise we're going to be focusing on a paper today specifically for all you kagglers out there it's called foundational large language models and text generation so get ready folks yeah we're going to see just how these things work we're talking AI that can you know write code translate languages yeah solve really really tough math problems it's crazy it's really a wild ride how

we got from their humble beginnings to today's you know absolute Superstars like Gemini I got to ask how do we get from these I don't know simple language tools to AI That's practically writing Symphonies that's a huge jump well it all starts with this idea that language models are basically um just predicting what word comes next in a sentence sounds sounds simple right right but that's the the Bedrock of everything that they do from translating you know languages to writing code it's all about understanding these relationships between words okay that makes sense but how did

they get so smart didn't they used to be like way slower and I don't know less capable yeah so you're thinking of the old rnn's recurrent neural networks they were kind of like reading a book like one word at a time you know which is fine for short stuff but really slow for big chunks of text right and then came Transformers and they completely like change the game Transformers are like reading a whole paragraph at once you know and just grasping the meaning instantly interesting it's all thanks to this thing called the self attention mechanism

Transformers self attention yeah these names are kind of sci-fi not going to lie they are a little bit yeah yeah they are can you break it down for us what is this what is self attention so imagine you're at a noisy party and you're trying to follow like three different conversations at the same time oh been there yeah exactly and your brain is focusing on different parts of each chat to kind of get the gist right right that's basically what self attention does for a Transformer okay it zeros in on you know the most important

parts of whatever input it's getting okay I'm starting to get it but this self attention does it really work like our brains that seems kind of I don't know far-fetched yeah so let's test it out right take the sentence the tiger jumped out of tree to get a drink because it was thirsty okay a thirsty tiger got it yeah and we instantly know that it refers to the tiger right not the tree not the tree yeah so self attention helps the model to make that same connection oh just like we do interesting yeah okay so

the text is prepped the attention mechanism is doing its thing yeah what else is going on inside this Transformer what makes it tick so think of it like a really high-tech Factory with different stations right right and first you've got to prep the text kind of like ingredients for a really complicated recipe so this involves tokenization right like breaking down the text into bite-sized chunks and then embedding uh which is turning those chunks into numerical representations that the model can understand yeah and then finally uh positional encoding okay so the model doesn't forget the order

of those chunks because sequence matters it does all right so our recipe is ready now what time to cook up some AI magic yeah now this is where the multihead attention mechanism steps in it's really like the heart of the Transformer okay where it's analyzing these relationships between words in a sentence right kind of like figuring out which words are are related to each other it's how the model gets context okay and understands meaning so it's kind of like a detective piecing together Clues right yeah absolutely okay but we've been talking about processing text how

does it transform actually learn from it okay so that's where training comes in Imagine like teaching a kid to read you give them books they try to read you correct them they learn right it's similar with Transformers except they devour like mountains of digital text right so we're talking massive digital libraries right oh yeah but what's happening behind the scenes during this training process is it just reading and memorizing it's more like a feedback loop okay we feed the model batches of text it tries to predict what comes next we check its answers right and

based on how well it does the model tweaks its internal settings kind of its parameters to get better at predicting so it's constantly kind of learning and improving that's so cool but how do these different Transformer architectures like gbt and Bert you know change the learning game are they all trained the same way that is a great question they actually all have kind of their own learning style tailored to their strengths so think of GPT the the decoder only model kind of like a Storyteller it's training it's really focused on predicting the next word in

a sentence kind of trying to like finish your story for you okay so GPT is like a novelist exactly got it what about Bert the encoder only model Bert's more like a Puzzle Master ooh okay it's trained to fill in missing words or guess the next sentence right and it really excels at understanding the the context and relationships between words got it so different learning styles for different tasks exactly that makes sense speaking of different models we've seen so many llms pop up in the last few years it's like a whole family tree of AI

can we can we trace this Evolution from like gpt1 to the latest and greatest like Gemini absolutely so let's start with you know gpt1 the OG right one of its biggest breakthroughs was uh unsupervised pre-training this meant that the model could like learn from from tons of unlabeled text basically teaching itself so it's kind of like learning a language by immersing yourself in it right no textbooks just pure exposure exactly yeah got it what about its successor gpt2 how did it up the anti so gpt2 went big okay it was trained on a data set

10 times larger than gpt1 wow and had 10 times the number of parameters okay it was like giving the model a super sized brain okay and a whole universe of text to learn from wow talk about a growth spur what kind of impact did that have on its ability oh it was mindblowing so gpt2 could generate super realistic uh and coherent text mimicking human writing styles like with with scary accuracy wow yeah it was like the model had absorbed all of this knowledge could now like Express itself really eloquently so went from like babbling to

writing poetry basic yeah wow and then came gpt3 which made even bigger waves what were the game changers with GP 3 so GB3 just kept the scaling Trend going with a a mindboggling 175 billion parameters 175 billion wack but it also brought something new to the table F shot learning so it could understand and perform tasks with just a few examples oh wow yeah so no need for those like mountains of labeled data exactly that's incredible it's like learning a new skill just by watching like a quick demo or something yeah so efficient yeah what

about GPT 3.5 and 4 how does did they build on this foundation so they really doubled down on dialogue okay and added multimodal understanding moreable okay which means that GPC 4 can actually handle images and text wow yeah which is a huge step forward huge and they also expanded that context window so they can remember longer conversations or or documents okay gpt's evolution is super impressive it is but Google's been in the game too right oh yeah what about their models tell me about lamb da ah Lambda yeah language model for dialogue applications Google's answer

to that conversational AI challenge right right so while you know GPT models are kind of like these multi-talented performers ler is like the smooth talker specializing in really natural engaging conversations so it's all about making that AI chat you know feel more human less robotic exactly I love that what about gopher deep mind's contribution to the uh LM party yeah so gopher was all about refining that training process they were super meticulous about data quality and and optimization okay yeah creating a model that's just a beast at like knowledge intensive tasks so it's not just

about like how me data you feed it right but also the quality and how you actually train it exactly that's interesting and speaking of training wasn't there a model chinchilla that kind of changed our thinking about scaling oh yeah chin CH yeah they really challenge the you know bigger is always better mentality right their research showed that scaling data and parameters equally is is crucial for for Optimal Performance oh interesting so you know you can have smaller more efficient models that still pack a-p punch that's a GameChanger efficiency matters Google must have taken note of

that with paulm right yeah their model is uh a bit of a behi isn't it paulm the pathways language model is definitely a heavy hitter with 540 billion parameters 540 cu trained on a massive data set of text and code wow and it can really tackle everything from like Common Sense reasoning to really complex code generation paulm sounds like a real powerhouse what about paulm 2 did they make it even bigger actually they focused on making it smarter paulm 2 has even better reasoning abilities and excels at things like um you know codenation and and

complex math problems and they actually did it with fewer parameters than paulm so talk about efficiency wow it's amazing how these models are getting smarter A and D more efficient it is but there's one more I'm dying to hear about the the Superstar of Google's llm family Gemini Gemini yeah Gemini is the the real deal it really combines the best of all of the previous models okay and takes that multimodal understanding you know to the next level we're talking text images audio video all processed sinlessly it's like the the ultimate AI Swiss army knife okay

that's officially mind blowing and with Gemini 1.5 they've expanded the context window too like millions of tokens yeah that's insane it's like you know having an AI with a photographic memory right it can process and recall information from from massive amounts of data wow you know connecting the dots drawing inferences across like entire books wow even movies so it's not just about understanding these little pieces of information it's about putting it all together and making sense of the big picture exactly that's wild but all this power all these capabilities seem like pretty inaccessible for most

people right is Google trying to change that they are with Gemini Nano they're actually bringing powerful AI capabilities to you know smaller devices like smartphones and wearables okay it's a testament to how fast this technology is evolving and becoming more accessible it's exciting to see llms moving beyond those research labs and and into our everyday lives but it's not just Google Making Moves In in open source llms we've seen other players like like meta and mistal AI stepped up to the plate right oh absolutely the the open source llm scene is booming models like L

from meta and mixol from mistol AI are giving you know researchers and developers really powerful tools to explore these you know frontiers of AI it's amazing to see this level of collaboration and Innovation you know yeah it's like the whole AI Community is working together to unlock the full potential of llm exactly and with xai's release of grock one we're we're seeing even more ambitious models going open source wow right so making these powerful capabilities available to to everyone it's a golden age for AI development yeah it's incredible to see how far we've come from

those those early days of Transformers to these these sophisticated multimodal llms right but it's not just about building bigger and better models is it we need to learn how to fine-tune them for specific tasks and truly unleash their potential exactly your spot on that's where fine-tuning comes in it's how we take these general purpose language models right and turn them into specialized tools that excel at specific jobs all right I'm all yours let's dive into this world of fine tuning and see how we can mold these powerful llms into like Precision Instruments yeah it's like

taking you know a raw diamond and cutting it just right right to unleash its Brilliance I love that analogy we'll explore techniques like you know supervise fine-tuning reinforcement learning from Human feedback and parameter efficient fine-tuning all designed to make these llms more effective and align with our goals Let's uh kick things off with supervised fine tuning okay or sft for short sft it's like sending our llm to a specialized training program you know to become an expert in a specific area so instead of being a jack of all trades that becomes a master of one

exactly I like it how do we make that happen so we give it a a crash course you know using a smaller okay carefully curated data set that's specifically designed for that task right so for instance you know if we want our llm to be a whiz at summarizing research papers we train it on a data set of papers and their corresponding summaries written by humans so it's learning from the best the expert summarizers exactly yeah okay once our llm has a sft what's the next step in this fine-tuning boot camp right so that's where

reinforcement learning from Human feedback comes in okay R lhf for those who like acronyms rhf okay that sounds intense is it like a personal trainer for our llm pushing it to reach its full potential yeah yeah yeah so in our lhf we actually use human feedback to train a reward model okay this reward model acts like a judge you know learning to tell the difference between good and bad good and bad responses from the llm got it so it's like a talent show the llm is performing and the reward model is giving it scores yeah

based on the judges preferences exactly I love that analogy and this feedback then helps us to you know fine-tune the llm even more encouraging it to generate responses that that really hit the mark it's like the LM is constantly trying to impress the judges get a standing ovation yeah yeah yeah that's such a cool concept but you also mentioned something called parameter efficient fine-tuning right or PFT what's that all about yes so PFT tackles a uh a real world problem you know fine-tuning these massive llms can be computationally you know really expensive right imagine having

to retrain a model with you know billions of parameters just to teach it a new trick right PFT offers a much more efficient way so instead of retraining the whole model we can focus on fine-tuning specific Parts exactly right that sounds a lot less daunting yeah yeah so PFT techniques like adapter and lauro let us add these small specialized modules to the llm without messing with its kind of core structure got it so it's like adding a few specialized tools to our llms toolbox right instead of rebuilding the entire Workshop exactly very clever so we've

fine-tuned our llm it's ready to go yeah but how do we actually use it how do we talk to it tell it what to do right so that's where the art of prompt engineering comes in prompt engineering yeah it's all about crafting these uh effective instructions the prompts okay to guide the llm and get the results we want so it's like giving our llm a clear recipe yeah you know with step-by-step instruction to create the perfect dish precisely so the way we phrase our prompts okay can make a world of difference interesting a well-crafted prompt

can actually nudge the llm to generate more accurate more relevant and more creative outputs so it's not just about what we ask right but but how w we ask it exactly are there different types of prompts that we can use to get different results yeah there are a few tricks of the trade okay let's hear them so we have zero shot prompting where we just give the llm a task description right and let it use its existing knowledge to to come up with the response okay then there's fuse shot prompting where we give it a

few examples to kind of steer it in the right direction got it and finally there's Chain of Thought prompting okay where we actually walk the llm through how to solve similar problems like step by step it's like we're teaching it to think like a detective yeah breaking down the problem following the clues exactly yeah okay but once we've given the llm a prompt how does it actually choose what to say right so that's where sampling techniques come in sampling techniques sorry and they control how the llm picks the next word in a sequence kind of

influencing the style and the iety of its output so it's like choosing the right words to paint a picture yeah you know some techniques create a like realistic image right others go for a more abstract style exactly so we have techniques like greedy search Okay always picking the most likely word okay which leads to predictable right but you know kind of safe outputs safe got then there's random sampling throwing some uh unpredictability into the mix and then techniques like temperature sampling and topk sampling allow us to kind of fine-tune that balance between predictability and creativity

it's fascinating how we can guide the llms creative process yeah it is but uh let's talk about something a little bit more practical as these llms get bigger and smarter actually getting responses from them the inference part right seems like it could take forever yeah you're absolutely right as as llms grow the computing power and memory they need just like explodes and this can lead to you know longer wait time and higher cost which isn't ideal for real world applications right right so how do we deal with that how do we speed things up and

make these powerful models more accessible for everyone right so that's where infr optimization techniques come in okay and uh researchers and Engineers are constantly finding these clever ways to make inference faster cheaper and more practical that sounds promising yeah can you give us a sneak peek into some of these techniques yeah so one approach is quantization quantization just like streamlining the model's internal calculations without sacrificing too much accuracy okay so by using lower Precision numbers we reduce the memory footprint and speed things up so it's like using Shand yeah to make the calculations quicker right

without losing the meaning exactly that's clever are there other ways to make llms more efficient yeah another technique is distillation distillation okay where we train a smaller faster uh student model okay to mimic the behavior of a larger uh teacher model so it's like you know passing on the wisdom of a master to a promising Apprentice oh I like that so the student model learns the tricks of the trade right from the expert yeah and can then perform the task more efficiently exactly that's really cool what other techniques are out there so they're also output

presing methods output preserving okay which guarantee that uh the model's response remains unchanged unchanged okay one example is Flash attention and it's a way to optimize like a core part of the Transformer the attention calculation without affecting that final output so it's like fine-tuning the engine of a car to make it more fuel efficient without changing how the car drives exactly that's impressive what about prefix caching what does that do yeah so prefix caching is is super helpful for things like chat Bots or you know when you're anal ing long documents it lets us store

and reuse previous calculations for parts of the input that haven't changed you know saving time and resources it's like remembering the answers to questions we've already asked yeah instead of having to figure them out again and again exactly smart what about speculative decoding how does that speed up inference so speculative decoding is a bit like having a team of assistants working in parallel okay it uses a smaller faster um dra model right to predict multiple tokens ahead okay which the main model then double checks so it's like brainstorming ideas yeah generating multiple possibilities and then

having the expert pick the best one exactly I like that yeah with all these optimization tricks it sounds like llms are becoming more practical by the day they are now I'm even more curious about how they're actually being used in the real world yeah what are some of the most exciting applications out there so llms are already making you know a huge impact in in so many areas from code generation to machine translation text summarization question answering and even creative content generation wow it's like having this super power assistant for almost every task imaginable that's

amazing I'm I'm especially interested in how llms are you know shaking things up in the world of code and Mathematics yeah are they really becoming these valuable partners for developers and mathematicians absolutely in the realm of code llms are revolutionizing things like code generation right completion refactoring debugging and even like translating between programming languages wow yeah it's like having an AI pair programmer yeah exactly who can like anticipate your next move and help you write better code faster absolutely and what about math how are llms making their Mark there so they're showing incredible talent in

in tackling these complex math problems from you know basic arith Matic to to advanc Concepts and abstract algebra wow and geometry that's crazy these models are going Beyond just manipulating language they're actually reasoning about abstract mathematical Concepts it's like having an AI mathematician on your team yeah helping you solve those you know tricky equations absolutely that's that's mind-blowing and Beyond code and math llms are also changing the way we translate languages are we getting closer to that that Universal translator we've always dreamed of we are you know we're getting closer than ever right llms are

are making translations more fluent more accurate and more contextually aware they're really breaking down these language barriers and making Global Communication much smoother it's like having a conversation with someone from another country yeah without any like awkward misunderstandings that's amazing exactly and speaking of conversations llms are also powering you know this new gener are they those don't know Cluny Robo interaction traditional chat Bots you know we often limited by these rigid rule-based systems llms are bringing a whole new level of naturalness and dynamism to chatbot interactions oh okay making those conversations more engaging in humanlike

yeah exactly it's like the difference between talking to a machine and having a real conversation with a friend exactly and what about content creation how are llms impacting the way we create and consume content right are they going to like replace writers and artists so llms are definitely becoming you know powerful tools for Content creators okay assisting with everything from writing marketing copy and generating you know creative storylines to producing scripts and even like crafting these personalized messages they're more like you know creative Partners helping us express ourselves in in new and engaging ways so

it's like having an AI Muse yeah Whispering ideas in your ear yeah yeah yeah but llms aren't just creating content they're also getting better at analyzing right and understanding text right are they like becoming expert readers absolutely llms excel at tasks like natural language inference okay where they like figure out the logical relationships between sentences and text classification where they categorize text into these predefined groups so they're not just generating text they're dissecting it understanding the nuances extracting valuable insights that's incredibly powerful what kind of like real world applications are we seeing with this level

of understanding so these capabilities are being used in areas like sentiment analysis figuring out you know how people feel about something based on their writing right uh legal document review and even like medical diagnosis wow where llms can help experts make more informed decisions it's inspiring to see llms being used to tackle real world challenges in so many different fields MH but we can't forget about the the rise of multimodal llms right the ones that can handle more than just text right what's happening in that space what kind of exciting applications are we seeing there

yeah multimodal llms it's like you know opening a door to A Whole New World right they ability to process and generate content across text images audio video it's crazy is creating possibilities we can only dream of before it's like we're stepping into this multimedia Universe yeah where AI can understand and interact with information in all its forms yeah yeah what kind of amazing things can we expect from these multimodal llm so imagine like AI systems that can create captivating stories from images personalized educational experiences that combine you know text and visuals or even like assist

doctors in diagnosing medical conditions by you know analing scans and and patient records those applications sound absolutely incredible it feels like we're on the verge of a major breakthrough in AI yeah where where machines can understand and interact with the world in ways that we never thought possible Where Do We Go From Here what's next for llms so with the you know the speed of innovation in this field right the future of llms is just like incredibly exciting new architectures are being developed training techniques are are becoming more efficient and applications are are popping up

in every imaginable domain it's an exhilarating time to be following the world of llms it's like we're riding the wave of a technological Revolution with AI playing a you know a bigger and bigger role in our lives absolutely but with all this progress it's important to keep in mind that llms are still just tools right they're incredibly powerful tools yes but tools nonetheless yeah what are your thoughts on that yeah you're you're absolutely right as we continue to develop and refine these you know powerful AI systems we we have a responsibility to make sure that

they're you know used ethically and for the benefit of society it's up to us to to guide their development and ensure they're used for good we've covered a ton of ground in this in this deep dive it's been a wild ride from the the nuts and bolts of Transformers to the uh to the just mindblowing capabilities of uh you multi llms yeah it really it really highlights the uh the incredible pace of innovation in in AI yeah new models training techniques applications constantly popping up pushing the the boundaries of what we thought was even possible

it's definitely an exciting time to be to be following uh the world of llms but before we wrap up let's uh let's take a step back and look at the big picture okay what are the the key takeaways from from our journey into this this amazing world of AI so I think one of the uh one of the biggest takeaways is is really the power of scale we've seen how you know increasing model size and data volume has led to these huge leaps in in llm capabilities like they're getting smarter and more capable with every

you know gigabyte of data they consume it's like they're on a constant learning spree yeah but it's not just about stuffing them with information right right data quality and uh and efficient trading techniques are crucial too absolutely carefully curating the data and find maintaining that training process is essential for creating llms that are both powerful and efficient it's not just about quantity it's about quality and and strategy and and we can't forget about fine-tuning those techniques that transform these you know general purpose models into like these specialized tools for specific tasks it's like giving them

a set of skills tailored uh for a particular job exactly techniques like you know sft and rlf allow us to to mold these llms into you know experts in in various domains whether it's summarizing you know complex research papers or engaging in in natural humanlike conversations exactly and as these models get more powerful we we have to keep up with the with the Practical challenges of actually using them yeah speeding up those response times making them more accessible is is crucial for for real world applications right that's where those you know clever optimization techniques come

in quantization distillation speculative decoding right they're all helping to to make llms more uh more practical and and readily available for everyone it's fantastic to see these these advancements making llms more more powerful more efficient and more accessible yeah but amidst all this this excitement it's important to to remember that llms are tools right and like any powerful tool they need to be used responsibly you're you're right we we have a responsibility to make sure that these incredible AI systems are are used ethically and for the benefit of of society it's it's up to us

to to guide their development and and ensure they're used for good absolutely well we we've explored the inner workings of Transformers traced the evolution of llms and even glimpsed into the uh the future of multimodal AI has been an incredible journey it really has it's a it's been a pleasure sharing these insights with you and uh and our listeners the world of llms is is constantly evolving so I I encourage everyone to stay curious keep exploring and uh and let's continue to shape the uh the future of this this transformative technology together and that wraps

up another episode of The Deep dive until next time keep learning keep questioning and keep diving deep into the world of knowledge