Right, ready to dive into some seriously cool AI research.
Absolutely, always up for that.
Awesome.
Today we're looking at main rag,
which stands for multi-agent filtering
or retrieval augmented generation.
Catchy name, right.
It is, but what's even cooler is what it does.
It's all about making large language models.
You know those AI systems that can like understand
and generate text, even smarter and more reliable.
Yeah, those LLMs can be pretty amazing,
but one of the things that this research really dives into
is how to deal with the fact
that sometimes they make things up.
Oh yeah, or they give you info that's totally outdated.
Exactly, and that's because they're working
with limited data, so main rag is trying to fix that.
You know how annoying it is to ask your AI assistant something
and get an answer that just feels off?
Oh yeah, totally.
Like wait, that doesn't sound right.
Yeah, exactly, like imagine asking for directions
and your AI sends you down a road
that's been closed for years.
Oh, that's the worst.
Right, and it's because its knowledge is stuck in the past.
So to make AI more accurate, researchers have been using
something called retrieval augmented generation,
or AG for short.
Okay, so AG gives these LLMs access
to more up-to-date info from like external sources.
Yeah, think of it like giving the AI access
to a whole library of information.
But I'm guessing it's not as simple
as just giving it access to everything, right?
You're right, even with AG you can still end up
with a lot of irrelevant or unreliable information mixed in.
Ah, so it's like trying to write a report
with a bunch of random pages
scattered throughout your research.
Exactly, it can make it really hard to find what you need,
and that's where main rag comes in.
So main rag is like a quality control system,
making sure that AI is using the best possible information.
Exactly, it uses a team of three AI agents
to filter and rank the documents it pulls in
from outside sources.
Oh wow, three AI agents, sounds like a team effort.
It is, think of them like specialized researchers.
Okay, I'm intrigued, tell me more about these agents,
how do they work?
All right, so let's start with Agent One,
this one is called the Predictor.
Okay, the Predictor, what does it do?
Well, it takes the question that you've asked,
and then tries to come up with possible answers
based on each document that it finds.
So it's like each document is being interviewed
for the job of providing the best answer.
Yeah, that's a great way to think about it.
And then once the Predictor has generated
these potential answers from each document,
Agent Two steps in, this one's called the Judge.
Okay, the Judge, this is where it gets interesting.
Great, so the Judge analyzes each document,
it also looks at those potential answers
that the Predictor came up with.
I see.
And its job is to figure out
if the document actually supports the answer
and how relevant it is to the question.
So it's like the Judge is checking the facts,
making sure everything lines up.
Right, and then based on this whole analysis,
the Judge assigns a score to each document.
This score is super important
because it helps determine which documents
are trustworthy and which ones aren't.
Ah, so it's like a ranking system.
The most reliable sources get the highest scores.
Exactly, and this is where the Adaptive Judge bar comes in.
Okay, Adaptive Judge bar, tell me more.
So think of this bar as like setting a standard
for how trustworthy a document needs to be.
Okay, I'm following.
So anything below the bar gets filtered out.
So it's like a filter only letting in the most relevant
and reliable information.
Yes, but here's the really clever part.
This bar isn't fixed.
It actually adapts based on the scores
that the Judge is giving out.
Yeah, so if the Judge is seeing
that a lot of the documents are scoring high,
meaning they're really relevant to the question,
it raises the bar.
Makes sense.
So only the best of the best get through.
Exactly, but you know,
if most of the documents aren't great,
well, the bar gets lowered.
Okay, so it's still filtering out
the worst of the worst just being a little more lenient.
Yeah, exactly.
It's not gonna let in total garbage,
but it's also not gonna be super strict
if there's just not much good info out there.
So this Adaptive Judge bar,
it's kind of the secret sauce here, right?
Yeah, I think so.
You know, it means that main rag
is constantly learning and adapting.
It's getting smarter about what information
it can actually trust.
Exactly, and you know, this approach is really cool
because it doesn't need tons of data
or training to learn how to do this.
So it's like a plug and play solution to make AI smarter.
Pretty much.
That's really impressive.
But I'm curious,
if we're letting in even slightly irrelevant documents,
wouldn't that make the AI more likely to mess up?
You'd think so, right?
You'd think a little noise would throw things off.
Yeah.
But even when the judge isn't 100% certain
about a document, it still gives higher scores
to ones that have at least some useful information.
Oh, interesting.
So even if a document isn't a perfect match,
it might still have some valuable insights.
Yeah, exactly.
It's all about extracting as much knowledge as possible.
I see.
So main rag is able to like squeeze every drop
of relevant information out of the sources it can find.
It's not just looking for a perfect match.
Right, it's like looking at the bigger picture.
Okay, that makes a lot of sense.
So now we've got these documents,
they've been filtered and ranked, what happens next?
Right, so that brings us to Agent Three.
This is the final agent in this whole system,
and it's called the final predictor.
The final predictor, okay.
And so this agent, it takes all those filtered
and ranked documents from the judge,
and then uses that information to give the final answer.
The hopefully more accurate answer,
thanks to all that filtering and ranking.
Exactly.
Okay, I'm really liking this team effort approach,
but does it actually work?
I mean, what kind of results did they see?
Oh, it definitely works.
They tested main rag on four different
question answering tasks.
Okay, cool, four different tasks.
Like one of them was open domain question answering.
So that's where the AI has to like search
through a massive database to find the answer?
Yeah, exactly.
And they even tested it
on a long form question answering task.
So those are the questions where the answers
are like more complex,
and they need a deeper understanding of the topic.
Right.
Okay, so tell me, how did main rag do?
It consistently outperformed other systems.
Seriously.
Yeah.
Sometimes by as much as 11% in accuracy.
Wow, that's a big difference.
It is, and it even beats some systems
that had a lot more training.
So it's not just more accurate, it's also more efficient.
Yeah, and it did all of this
while using fewer computational resources.
That's amazing.
So this is all very impressive,
but what does it actually mean for the average person?
How could this change the way we interact with AI?
Well, imagine if your search engine
could always give you the most relevant
and up to date results.
Oh yeah, no matter how obscure or weird your search is.
Exactly, or think about those customer service chat bots.
Ugh, those can be so frustrating.
I know, but imagine if they could actually understand
your questions and give you accurate answers.
And not have to transfer me to a human
like five minutes into the conversation.
Right, this is the kind of future
that main rag could help create.
Okay, yeah, that would be a game changer for sure.
But what about other areas of AI?
I mean, beyond just question answering,
could this technology be applied to other tasks as well?
Absolutely, it could totally revolutionize
a whole bunch of AI applications.
Well, think about things like summarizing complex documents
or translating languages.
But even more, think about generating creative content.
Content that's actually original and informative.
Exactly, the possibilities are huge.
Okay, now I'm really excited.
But before we get too carried away
dreaming about the future,
can you maybe give us a bit more detail
about how main rag actually works in practice?
Sure.
Can you walk us through a specific example?
Of course.
So let's take a look at how main rag handles real world questions.
Okay.
I've got a few case studies here
that really show how it filters and ranks information
to get to the right answer.
All right, let's hear them.
I'm ready to see main rag in action.
Okay, hit me with those case studies.
I'm dying to see main rag in action.
All right, let's start with a question
from this data set called PopQA.
It's all about those like random trivia questions.
You know the ones that make you go,
huh, I wonder about that.
Yeah, yeah, I know exactly what you mean.
So the question is in what city was Monsumeranda born?
Ooh, okay, you got me.
Yeah.
I have no idea.
Me neither.
But don't worry, main rag was up for the challenge.
Okay, good.
So it pulls up all these documents about Monsumeranda.
Who is Monsumeranda?
Turns out he's a Spanish pole valter.
Okay.
And some of these documents mention his birth year
or they talk about his childhood,
but only one actually says he was born in Santers, Spain.
Wow, so it's like finding a needle in a haystack.
Exactly.
But here's where that adaptive judge bar comes in.
Since most of the documents didn't actually have the answer,
the bar was set a little bit lower.
Right, makes sense.
But Agent Two, the judge, was still able to pick out
that one document that directly answered the question.
And give it a much higher score so it stands out.
Right.
So even with all that irrelevant info,
main rag could still find what it needed.
Exactly.
And that's how Agent Three, the final predictor,
confidently gave the right answer, Santers.
Very cool.
But what happens when there are multiple documents
that have the right answer?
Does main rag just pick one at random?
That's a great question.
Let's look at another example,
this time from the Trivia QA data set.
The question is, where is the area of 127,000 square kilometers
in central South Australia,
where the public is not allowed
under Australian Defense Force rules?
Oh, wow, that's super specific.
Right, you need some pretty new knowledge
to answer that one.
I bet.
So for this one, main rag found a bunch of documents
about these restricted areas in Australia.
Okay.
And a lot of them mentioned this place called
the Wumer Prohibited Area.
Which fits the description.
Exactly.
So how does main rag choose which document
to actually use when there are multiple ones
that have the right answer?
Well, remember how the judge assigns those scores
based on how certain it is about a document?
Yeah.
Well, that's where this gets interesting,
because even though multiple documents might talk
about the Wumer Prohibited Area,
some of them might have given stronger evidence
or more detail.
I see.
So it's not just about finding the answer,
it's about finding the best source for that answer.
Precisely.
So by ranking the documents
and putting the most relevant one first,
it makes sure that the final predictor
has the best possible information.
Like main rag is curating the information,
almost like a librarian would do for a research project.
Yeah, that's a great analogy.
It's not just retrieving documents,
it's like carefully selecting and organizing them
to make sure the AI gets the best possible results.
This has been a super fascinating deep dive.
I feel like I have a much better understanding
of how we can make AI smarter.
Any final thoughts before we wrap things up?
Yeah, I think the big takeaway here is that the future of AI
isn't just about creating these bigger
and more complex models.
It's also about finding smarter ways
to manage and process information.
Main rag is a really great example of that,
showing that we can use AI to help solve
some of the problems that AI creates.
It's like AI becoming self-aware and improving itself,
which is both exciting and a little bit mind-blowing
to think about.
I know, right?
It's really cool stuff.
Well, thanks for guiding us through this research.
It's been a really insightful deep dive.
My pleasure.
Always happy to talk about the latest in AI research.
And to all our listeners, until our next deep dive,
keep those brains curious.