Okay, today we are diving deep into something
that's absolutely foundational to the AI tools
changing our world right now. Large language
models, LLMs. And they truly are the engine behind
so much of what's called generative AI. You know,
the AI that isn't just analyzing data but actually...
Creating new stuff exactly creating and our source
material for this deep dive It's a really solid
article from hack science dot education called
large language models Yeah, it's a good one and
our mission today is really to cut through the
noise Pull out the essential concepts and give
you those genuine aha moments about how these
seemingly magical tools actually work without
getting totally lost in the weeds, you know?
Right. Think of it as getting the core operating
manual. Just explain clearly. Love that because,
yeah, sometimes they feel like pure magic. So,
all right, let's unpack this. At the most fundamental
level, what are these LLMs? OK. So at their core,
LLMs represent a pretty significant leap forward
in AI. They're specifically designed to generate,
well, human -like text. But the really crucial
idea here, I think, is that they're built upon
what are called foundational models. Foundational
models. Okay, that sounds important. Why? They
really are. Because these foundational models,
they're trained on just absolutely massive amounts
of diverse data. This gives them a broad general
knowledge base. And this foundation enables something
powerful called transfer learning. Transfer learning,
okay. Yeah, so instead of building a completely
new model for every single task, you can take
this one foundational model and sort of Adapt
it for many different things. Ah, okay So one
giant model train like crazy can then pivot to
writing a screenplay or summarizing a legal doc
or maybe even generating computer code that Versatility
comes from the foundational model and that this
transfer learning precisely. It's a unified approach
that allows for incredible flexibility across
domains And, you know, that deep broad training
also makes them surprisingly good at spotting
nuances like grammar issues or inconsistencies.
The key word really is generative. They create
new content based on all that knowledge. OK,
creating new text. But how? I mean, how do they
take all that training data and actually produce
coherent, you know, human sounding sentences?
Well, the core mechanic is actually surprisingly
simple to grasp, conceptually at least. The scale
is mind boggling. But the idea is this. LLMs
work by predicting the probability of the next
word in a sequence based on the words that came
before it. They learn these patterns by analyzing
truly vast amounts of text data during training,
basically, soaking up books, articles, websites,
just all sorts of written material. So it's constantly
thinking, OK, given these words, what's the most
likely next word to follow? Exactly. And during
that training process, the model is constantly
tweaking its internal structure. potentially
trillions of tiny connections to make those predictions
more accurate, closer to the patterns it sees
in real text. So when you ask it to generate
something, it typically picks the word with the
highest probability, then uses that word as part
of the sequence to predict the next one, and
so on and so on. Word by word, building the response
based on probability. It's kind of like auto
-correct on steroids, right? Predicting the best
next step in a really long chain. Yeah, that's
not a bad analogy. But. processing that much
data and making those predictions effectively.
That requires some serious technical horsepower,
doesn't it? Oh, absolutely. And the real breakthrough,
the kind of aha moment here, was the development
of a specific architecture called the transformer
architecture. Transformer. Yes. This is what
fundamentally enabled the creation of these powerful
foundational models and consequently the LMMs
built upon them. So that's the secret sauce.
the transformer. In large part, yeah. It really
revolutionized how models handle sequences of
data like text. It allowed them to weigh the
importance of different words in the input sequence
much more effectively than previous architectures
could. Okay, so the transformer lets them read
and predict effectively at scale. Got it. So
what tasks can we actually use them for? What
does this predictive capability really unlock?
Well, because they are essentially foundational
models adapted specifically for natural language
processing or NLP and language generation, their
capabilities are incredibly broad. The power
really lies in them being general purpose. They
can tackle many different tasks without needing
entirely separate models or, you know, vast amounts
of specific training data for each individual
one. Okay, so give us some examples then, maybe
from the source. What kind of things can they
actually do? Sure. With the right input, they
can answer questions, draft documents like essays
or reports, summarize lengthy texts, translate
between languages, and even, as you mentioned,
generate computer code. Generate code. That versatility
is just kind of mind -blowing. It is. And their
real -world applicability spans across industries.
Think about summarizing customer feedback or
classifying emails, powering sophisticated chatbots,
drafting marketing copy, assisting with data
analysis, or even extracting specific like names
or dates from large documents. They can be applied
to almost any task that involves text, really.
They sound incredibly capable, almost like they
know everything. But they don't, do they? There
must be limitations. Oh, absolutely. A critical
limitation to understand is the training cutoff.
This refers to a specific date. The model's knowledge
base is built only from the data it was trained
on up until that point in time. So if a model's
training data ended in, say, late 2023, it won't
know about anything significant that happened
in 2024. Precisely. It has zero information about...
events, discoveries, cultural shifts, anything
that occurred after that cutoff date. Its understanding
is basically a snapshot of the world as it existed
when its training finished. Right. That's why
you can't ask most public LLMs about the latest
news or very recent scientific breakthroughs
and expect an accurate up to the minute answer.
That's a super important point for anyone using
them. Definitely. OK, so they're based on foundational
models. They predict words. They use transformers.
They have this knowledge cutoff date. Are all
LLMs fundamentally the same, though? Or are there
variations? Yeah, the source highlights a few
distinct types. First, you have the base LLMs
we've mostly been discussing, the versatile general
purpose models. They're good at many things,
but might not be perfectly tuned for very specific
or highly reliable enterprise tasks without some
further work. OK, the standard model. Got it.
Then you have instruction -based LLMs. These
also use a base model, but they're designed to
follow explicit instructions given in your prompt
very, very directly, like write this in the style
of Shakespeare or extract the key dates from
this text. You're giving it clear commands. Ah,
so you're being very specific about how you want
it to generate the output. Exactly. And then
there are fine -tuned LLMs. This is where you
take a base model and you train it further on
a smaller, highly specific data set, something
related to a particular task or domain. So like
you might fine -tune a base model using thousands
of medical research papers, maybe, or internal
company documents. Right, exactly. The goal is
to make it exceptionally accurate or helpful
for that specific area, improving its performance
beyond what the general base model could achieve
on its own. Now this does require additional
data and training time, of course, but it results
in a more specialized model. Base, instruction
-based and fine -tuned. That structure makes
a lot of sense. Now I've also been hearing quite
a bit about small language models or SLMs? Are
they just like smaller versions of these big
ones? Essentially, yes. SLMs are basically scaled
down LLMs. They're designed to be more resource
efficient, easier to deploy, more accessible,
while still providing many of the benefits you
get from the larger models. So maybe they're
faster, cheaper, but maybe less capable than
the giants like GPT -4. Is that the tradeoff?
Well, they are generally smaller, yes, and require
fewer computational resources, lower cost to
train and operate. And techniques like knowledge
distillation, where you train a smaller model
to mimic a larger one and transfer learning,
help them perform pretty well on tasks like analysis,
translation, summarization. But here's where
it gets really interesting, and this is a significant
aha from the source material. The success of
some SLMs, like the PHY series from Microsoft,
for example, isn't just about being smaller.
It's about strategic data selection. Strategic
data, you mean they don't just use all the data
they can find? No. And this kind of flipped a
lot of conventional thinking on its head. Instead
of just prioritizing sheer quantity of training
data, they focus intensely on quality. Quality
over quantity. Exactly. This involved using things
like textbook quality educational data, carefully
curated synthetic data sets, and really select
web data. So it's like teaching a student with,
I don't know, high quality textbooks instead
of just letting them browse the entire internet
randomly. That's a great analogy, actually. This
approach focused on building common sense reasoning
and a solid general knowledge foundation from
these highly curated sources. And the surprising
part is that, in some cases, SLMs trained this
way have been shown to match or even surpass
the performance of much, much larger models on
certain specific tasks. Wow. OK, quality over
quantity really can make a difference, even at
this scale. That's fascinating. All right, shifting
gears just a bit. How do developers and companies
actually use these models? Are they all locked
away by big tech or is there a way for others
to build on them? Yeah, the source differentiates
between open source and commercial LLMs. You
have companies like Microsoft with Azure OpenAI
and OpenAI itself offering powerful commercial
models. These are often designed for stability
and enterprise use. Thousands of businesses reportedly
build applications and services right on top
of these platforms. So that's how many companies
get access to the sort of leading edge models.
Exactly. But there's also a really growing ecosystem
of open source models that you can, in theory,
download and run yourself, assuming you have
the right hardware and technical know -how. And
interestingly, the source notes that the fundamental
concepts and how you actually interact with these
models, often through APIs or software development
kits, SDKs are quite comparable across different
providers, whether they're commercial or open
source. The basic ideas are similar. Right. Okay,
speaking of interacting, how do we, as users,
tell these models what we want them to do? What's
the mechanism? That's done through prompts. A
prompt is simply your input text, usually just
in natural language, describing the task you
want the model to perform. And the output you
get back is also text. So if I wanted to write
an email, I just type, like, write an email asking
my boss for a raise. That's simple. Pretty much,
yeah. That's the power of the natural language
interface. It feels intuitive. Now, the art and
science of designing and crafting those prompts
effectively is what's called prompt engineering.
Prompt engineering. OK, that sounds a bit like
programming, but using words instead of code.
It's often described exactly like that, yeah.
A new paradigm in programming the model. It involves
understanding the model's capabilities, how it
was trained, and how it tends to respond to different
phrasing, different kinds of instructions. Effective
prompt engineering is really crucial because
a well -crafted prompt dramatically increases
the likelihood of getting useful and relevant
output back. So it's not just about asking a
question. It's about structuring your request
in a way the model understands best. Giving it
context, maybe? Precisely. Prompts define the
task, but can also include specific instructions,
the content you want processed, maybe examples
of the desired output style, or even cues to
guide the model. And it's very much an iterative
process. Iterative. Yeah, you send a prompt,
see what you get back, and then you refine your
prompt based on that output to get closer and
closer to what you actually want. Trial and error.
Trial and error, to get it just right, makes
total sense. Okay. Now, when the model is processing
that prompt and generating text, what's the basic
unit it's actually working with internally? Is
it letters? Words? Good question. Those units
are called tokens. Tokens are the fundamental
pieces of text that LLMs process. They can be
whole words, sometimes parts of words, or even
punctuation marks. And tokens have practical
implications beyond just being the internal unit.
Crucially, yes. Tokens directly affect computational
costs. When you use a model through an API, for
instance, you're often charged based on the number
of tokens processed that includes the tokens
in your input prompt plus the tokens in the generated
response. Ah, okay. So longer prompts and longer
answers can actually be more expensive. Exactly.
And managing tokens is also really important
because the LLMs have a maximum limit on the
number of tokens they can handle at any one time.
This limit relates to their context window. The
context window, what's that? Yeah, the context
window is basically the amount of text, the sequence
of tokens that the model can see or consider
when it's making its prediction for the next
token. So it doesn't process the entire internet
simultaneously when it's answering me. It focuses
on a specific window of text around where it's
working. Right, exactly. The context window helps
the model understand the relationships and dependencies
within that immediate text sequence, which leads
to more accurate and coherent predictions. The
maximum size of this window varies significantly
between different models, by the way. It's another
constraint to be mindful of, especially because,
as the source notes, in practical applications
like RRag, the usable length of the context window
for your input is often shorter. You need to
leave space for the generated output as well.
Ah, that makes perfect sense. You need room for
the answer within that limited window. OK, so
we've talked. prompts, tokens, context window.
How does the model actually understand the meaning
of the words within that window? It's not just
matching patterns, is it? Not just patterns,
no. That's where embeddings come in. Embeddings
are these fascinating machine learning tools
used to represent text inputs, could be words,
phrases, even whole documents as numerical vectors.
Numerical vectors, like lists of numbers. How
does that help with meaning? Well, these vectors
are designed to capture semantic similarities
within a vector space. Think of it like this.
Imagine a giant, multi -dimensional map, where
every word or piece of text has a specific location,
a point. Words or texts with similar meanings
are located closer together on that map. So,
king might be close to queen on this map, and
maybe the vector distance between king and man
is similar to the distance between queen and
woman, those kinds of relationships. Exactly.
That kind of semantic relationship can be captured
mathematically. The closer the numerical vectors
are in this high dimensional space, the more
related their meaning is considered to be. These
vectors, which are just lists of floating point
numbers, are learned during the model's training
and act as a way for it to understand the semantic
essence of the text. The source mentions their
particular relevance in retrieval augmented generation,
RAG, techniques. Embeddings allow you to represent
external documents as vectors and then perform
semantic search searching based on meaning, not
just keywords to find relevant information before
feeding it to the LLM to generate an answer.
That's a really helpful way to visualize how
the model sees meaning. OK, so we use prompts.
It works with tokens within a context window.
It understands meaning through embeddings. Can
we influence the style or maybe the randomness
of the output beyond just tweaking the prompt?
Absolutely. That's controlled by model configuration
parameters. These are settings you can often
adjust that influence how the model generates
text and, importantly, its level of randomness
or predictability. Randomness. You mean like
how creative or surprising it is? Precisely.
Parameters like temperature and top are the main
ones that steer this randomness. A lower top
value, for instance, means the model is more
conservative. It only considers a very small
set of the most probable next words. This usually
results in more predictable focus text. OK. A
higher top, on the other hand, lets it consider
a wider pool of less probable words, which can
potentially lead to more diverse or creative
outputs. Interesting. And temperature. Temperature
works similarly. Higher temperature equals more
randomness, lower equals less. The source notes
that while temperature can technically go higher,
values above, say, 1 .2 tend to produce pretty
nonsensical text. It suggests the 0 .8 to 1 .2
range might be good for more creative tasks.
OK, got it. Temperature and top are like the
creativity sliders. What about those penalty
parameters mentioned? Frequency and presence
penalty. Right, so frequency penalty discourages
the model from repeating the exact same token
based on how often that specific token has already
appeared in the generated text. It basically
helps prevent it from saying the same word over
and over again. OK, avoids repetition. Yeah.
And presence penalty is broader. It discourages
the model from repeating any token that has appeared
so far in the output, regardless of frequency.
This tends to encourage it to introduce new concepts
or move on to different topics. So a high presence
penalty really pushes the model to explore new
ground within its response. So one stops repeating
the exact same word, the other pushes for new
ideas or topics. Roughly. Yeah, that's a good
way to think about it. And then there's legit
bias, which is a pretty powerful tool. It lets
you directly steer the likelihood of specific
tokens appearing in the output. How does that
work? You can effectively ban certain undesirable
tokens by giving them a very low score, like
an age of 100. Or you can make desired tokens
almost guaranteed to appear by giving them a
very high score, like 100. Smaller adjustments
just nudge the probability up or down a bit.
That sounds like you could really force it to
say certain things or completely avoid others.
You can, but the source rightly cautions that
it requires understanding how words map to specific
tokens, which isn't always obvious. And excessive
or inappropriate use can easily lead to nonsensical,
repetitive, or even biased outputs. It's a precise
tool that needs careful handling. Power, but
you definitely need to know what you're doing.
Fascinating how much control you can actually
have over the output, though. It really is. And
finally, just to round things out, the source
briefly mentions a couple more related concepts.
Model adaptation, which is the broader idea of
customizing models for specific use cases like
the fine tuning we discussed earlier. Right.
And the really intriguing idea of emergent behavior.
Emergent behavior. What's that? Yes. These are
capabilities or behaviors that the model starts
to exhibit. were not explicitly programmed or
even anticipated by the developers during training.
They seemed to sort of emerge from the sheer
scale and complexity of the training process
itself. Wait, things the model can just suddenly
do that nobody actually designed it to do? That's
incredible and maybe a little spooky. It is.
It really highlights that we're still exploring
the full potential and maybe even the fundamental
nature of these incredibly complex models. Wow.
Okay, so let's try and wrap this deep dive up.
We've journeyed through the fundamentals of large
language models, starting with how they're built
on foundational models, enabling generative AI
and transfer learning. Their core mechanism of
predicting the next word based on probability.
Right, the revolutionary transformer architecture
that makes it all possible. Their vast capabilities
from answering questions to writing actual code.
The crucial limitation of the training cutoff
date, remembering they don't know recent events.
Definitely. The different types base, instruction
-based, fine -tuned, and the surprising power
of small language models, especially when driven
by strategic, high -quality data selection. Yeah,
that quality over quantity point is key. Plus
how we access them through open source and commercial
options. And how we interact using prompts, the
whole art and science of prompt engineering.
Exactly. The fundamental units they work with
tokens and how they impact cost, and the limits,
like the context window. how they grasp meaning
through embeddings, those vector maps. And how
we can steer their output using those configuration
parameters like temperature, top, and the penalties,
all operating within the context window. Phew.
Understanding these building blocks really does
seem key to making sense of this current wave
of generative AI and figuring out how to use
it effectively. Absolutely. It demystifies it
somewhat, doesn't it? It really does. Hopefully
this deep dive has pulled back the curtain a
bit and given you listening some valuable aha
moments about the core technology powering so
much of what you're seeing and probably using
today. So maybe a final thought to leave you
with. Considering those emergent behaviors we
just touched on and that surprising success of
quality data over sheer quantity in SLMs. What
unexpected applications or capabilities might
we see next? Things emerging from these models
that aren't even being specifically designed
for right now. That's a great question. What
stands out to you most from everything we've
discussed today? Something to think about.