Hey everyone and welcome back for another deep dive.
Today, we're gonna be taking a look at this paper.
It's called the Fancy TS Grounding Leaderboard.
Benchmarking LLM's ability to ground responses
to long form input.
Ooh, long form, that sounds good.
Yeah, that's the key here.
And we're always talking about factuality, right?
With AI. Yeah.
And this is really interesting
because it's not just like a regular paper,
it's actually a leaderboard.
So it's kind of like a competition
to see which AI is best at giving you accurate information.
Okay.
And the really wild thing is the length of the text
they're using to test this.
Like we're talking up to 32,000 words.
That's like, I don't know.
It's a lot.
To novella.
Yeah.
And the AI has to be able to answer questions
based on that and only that.
It can't just go on Google.
Right, so it has to.
Like closed book.
Yeah, closed book, totally.
Yeah.
So how does this leaderboard even work?
Like is there a panel of judges or something?
Well, it's not quite like a talent show with scorecards.
But the great thing is it's designed to be really thorough.
So they test all kinds of AI models.
The things, the brains behind like chat GPT, for example,
across all sorts of topics,
finance, medicine, law,
and they ask them to do a whole variety of tasks,
like summarizing, finding specific facts.
And the best part is it's all judged by people.
Oh, wow.
Okay, so there is a human element to this.
So it's not just, you know, AI greeting AI.
Right, because it's not just about giving any answer, right?
It has to be the right answer.
And it has to understand what's being asked
and be able to find that answer
in this massive, you know, document.
Right, so no more of those AI excuses of,
oh, I couldn't find the answer,
or the dog hate my homework.
Exactly, and they actually had this two step process
to make sure that that doesn't happen.
So the first is eligibility.
Like is the AI even trying to answer the question properly?
Gotcha.
So for instance, you know,
if you ask something about wind energy
and the AI just says, well, it's good,
but it has challenges, that's out.
It has to give you a real detailed answer
grounded in the document.
Okay, that makes sense.
And then what's the second step?
Ah, that's where the big guns come in.
They bring in other AI models, you know,
some of the names you might recognize,
Claude, Gemini, GPT-4.
Wow.
And they judge how accurate the answers are
compared to what's actually in the document.
And this way it's not biased by just using one,
you're using multiple AI judges.
So it's like a jury of their peers, but all robots.
That's kind of wild.
So what did they find?
Like were there any clear winners
in this AI factual Olympics?
There were some that really stood out.
Gemini, especially the flash version,
did really well overall.
Okay.
They tested the models on both, you know,
things they might have seen before,
and then brand new information.
Just to see how they adapted.
And Gemini was consistently up at the top.
So does that mean that like bigger or more complex AI's
are always better at being factual?
Well, it's not always that easy.
Because remember that first step,
the eligibility, weeding out the bad answers.
Right, yeah.
Well, when they factored that in,
some of the models that initially looked pretty good
actually dropped in the rankings.
No, I'm not sure.
So it's kind of like, you know,
showing your work in math class.
Right.
You can't just put down the answer.
You gotta prove that you understand how you got there.
That's a great point.
Yeah, it's not just about knowing the facts.
It's about how to use them.
So, okay, this leaderboard seems like a really big step forward
in figuring out how to make AI more trustworthy.
But what are like the limitations
of this kind of research?
Like what couldn't they address fully?
Well, there were a couple of things.
First, some of the documents that they used to test the AI
might have already been part of its training data.
Oh, interesting.
So you can think of it as accidentally giving
a student a test with questions they've already seen, right?
Right, yeah.
Not a perfect measure, but still valuable.
Because the questions they're asking are brand new.
So it's still testing how well it can apply its knowledge
in a new situation.
So it's like seeing if they can think on their feet,
even if they might have skimmed the textbook beforehand.
Yeah, exactly.
And then the other limitation is that this leaderboard
is really good at testing what they call grounded factuality.
Okay.
So it means checking if the AI can pull the right information
from the specific document it's given.
It's not testing like general knowledge.
Right, right.
Or if it can double check facts against other sources.
So it's kind of like saying, OK, AI, here's your little world.
Tell me what's true within these boundaries.
But in the real world, information's everywhere.
Exactly.
So it would be interesting to see in the future research
where the AI has to deal with maybe conflicting information
or verify facts from multiple places.
Right.
That's a whole.
That would be really challenging.
Yeah, be whole other level.
Yeah.
So it sounds like this research has really opened up
a lot of possibilities.
But there's still so much more to learn about how
to make AI truly reliable.
Absolutely.
And we'll be right back after a quick word from our sponsor.
Stay tuned.
Welcome back to the deep dive.
You know, we're talking about making AI more accurate
and reliable even when dealing with mountains
of information.
It's really amazing to think about the potential here.
Like imagine AI that could just go through tons and tons
of data and just tell you like, hey, this is true.
This is not true.
It'd be a total game changer for everything,
from like fighting fake news to making sure you're
getting the right medical information.
Oh, absolutely.
Like think about a world where you could instantly
check a social media post or a news article is accurate.
Just by running it through like an AI fact checker.
Oh, wow.
That kind of technology could be so powerful in the fight
against misinformation.
Yeah, it sounds amazing, but also a little scary.
Like, I mean, who gets to decide what's considered true
in the first place?
It feels like there'd be a lot of gray areas.
You're right.
It's definitely not like a simple solution.
Like, you know, building a truly, you know,
comprehensive and unbiased database of information,
that's a huge task.
But that's exactly why research, like this leaderboard,
is so important.
It's really, you know, laying the foundation
for developing these AI systems that are, you know,
not just smart, but also trustworthy.
So it's like we're teaching AI to be like a responsible citizen
of the information world.
Yeah.
You know, making sure they know all the rules of the road
before we let them drive.
Exactly.
And, you know, just like with any powerful tool we have
to make sure we're using it for good.
Right.
You know, we need to think about the potential downsides
and make sure that AI is empowering people,
not manipulating them.
Yeah, well said.
So besides fighting fake news, what are some other ways
this kind of AI could be used in the real world?
The possibilities are huge.
Like, imagine an AI that could summarize,
like, really complex research papers, you know,
making scientific knowledge more accessible to everyone.
Right.
Or think about legal documents.
AI could help you analyze contracts, you know,
making sure you understand the fine print
before you sign anything.
Yeah, it's like having a super-powered research system
that never sleeps.
Exactly.
And because this research is focused on AI
that can deal with these, you know, really long documents,
it opens up even more possibilities.
OK.
Imagine AI that can personalize your education
by pulling information from tons of different sources
and tailoring it to your specific needs.
Wow, that would be incredible.
It really does sound like we're only just starting
to understand what AI can do when it comes to,
like, using information responsibly.
We are.
This research is just, you know, one step on a much longer
journey, but it's a super exciting one.
So we've talked about, like, the big picture,
you know, implications.
But let's zoom back in on the research itself.
What are some key takeaways for our listeners?
Like, what do they really need to get about this leaderboard
and what it means for the future of AI?
Well, I think, first of all, this research shows
that we are getting better at measuring
how good AI is at understanding facts.
Right.
This leaderboard gives us a way to, like,
compare different AI models, you know, side by side,
and see which ones are actually doing the best job.
Right.
And that's crucial for driving progress in the field.
Yeah.
It's like having a standardized test for AI.
Yeah.
So everyone's playing on the same field.
Exactly.
And because it's a competition, it encourages developers
to keep pushing, you know, the boundaries
and make their models even more accurate.
Secondly, this research highlights
how important it is to ground AI's answers
and actual evidence.
Remember that two-step process?
Mm-hmm.
That makes sure that the AI isn't just pulling random facts
out of thin air.
Right.
It has to be able to back up its claims
with info from the document it's given.
So it's not enough to just be smart.
You've got to be able to show your work.
Exactly.
And finally, this research shows that we are making,
you know, real progress in teaching AI
to understand and reason about complex information.
Yeah.
Like, the fact that these models can process documents
as long as novels and still give accurate answers,
that's a huge step.
Yeah.
That's pretty amazing when you think about it.
We're basically teaching machines
to think like, you know, expert researchers
to be able to just dig through all this info and pull out the key facts.
Yeah.
And the implications of that are massive.
Imagine a world where AI can help us make sense
of all this information that's coming at us all the time.
Yeah.
You know, it could help us be better citizens,
you know, better students, and even more efficient workers.
I'm definitely seeing the potential here.
Yeah.
It's like AI is becoming our partner
in this quest for knowledge.
Yeah.
You know, helping us navigate like an increasingly
complex world.
Yeah.
That's a great way to put it.
But of course, we have to remember that AI is a tool.
Yeah.
And like any tool, you know, it can be used for good or bad.
And that's why it's important to keep having these conversations
about, you know, AI ethics and make sure
that we're developing these technologies responsibly.
Absolutely.
The powerful reminder that we need
to be thoughtful about how we're integrating AI into our lives,
you know.
But with the right approach, it has the potential
to make us, you know, smarter, more informed,
and ultimately better equipped to face the challenges of the future.
Well said.
This research is, you know, a fascinating glimpse
into that future.
And it's clear that there's still so much more to explore.
We've covered a lot in this D-Styfe,
from like the nitty-gritty details of the leaderboard
to, you know, its potential impact on society as a whole.
It's been a great discussion.
And I think it really highlights just how important it
is to have these open and honest conversations about the role
of AI in our world.
And speaking of conversations, we
want to hear from you, our listeners.
Yeah.
What do you think about this research?
What excites you the most about the potential of AI?
And what concerns do you have?
Join the conversation on our social media
and, you know, share your thoughts.
We're really eager to hear your perspective on the future of AI
and how it's going to impact our lives.
Thanks for joining us on this deep dive
into the world of AI factuality.
We'll be back next time with another fascinating look
at the latest developments in artificial intelligence.
Yeah.
Until then, stay curious.
All right, so we're back.
And, you know, we've been talking about how this AI leaderboard
is really kind of pushing the boundaries of, you know,
AI and truth, and seeing how well it can stick to the facts,
even when there's like tons of information.
Yeah.
But before we wrap up, I wanted to go back to something
we touched on earlier, the human element and all this.
Yeah.
We talk about how people are the ones writing the questions,
judging the answers.
But like, how do we actually teach AI
to be more factual in the first place?
It's not like they're born knowing
the difference between what's true and what's not.
That's a great point.
And that's actually where it gets really interesting,
because these large language models, the brains behind the AI
that we're talking about, they're trained on so much data.
I mean, just massive amounts of text, mostly from the internet.
So that gives them a huge vocabulary, you know,
like a general understanding of the world.
But that doesn't necessarily make them experts in truth.
Right.
So it's kind of like they've read every book in the library,
but they don't know which ones are actually based on facts
and which ones are just made up stories.
Exactly.
They need some help to kind of figure out what's reliable
and what's not.
And that's where the humans come in.
So are there teams of fact checkers out there,
like grading AI essays and giving them gold stars for accuracy?
Well, not exactly.
But there are definitely humans involved
in that training process.
One way is to create special data sets of text
that are really designed to teach AI about factuality.
So think of it like, I guess, like a textbook for AI.
But instead of grammar or vocabulary,
it's all about how to identify reliable sources
and how to support your claims with evidence.
So like AI school, but the subject is truth 101.
Exactly.
Yeah.
Another way to train AI is through something called
reinforcement learning.
It's kind of like, well, it's kind of like training a dog,
right?
Give them a treat when they do something good.
And over time, they learn to do that thing more.
So with AI, instead of a treat, it's
like they get some kind of positive feedback or reward
for giving an accurate answer.
So it's like, good AI, you found the right fact
in that massive pile of text.
Here's a virtual high five.
Yeah.
Exactly.
And this training, it's not a one time thing.
It's ongoing.
So as they get more data, as they get more feedback from us,
they're constantly kind of getting better
at figuring out what's true.
Wow.
So it really is this amazing kind of collaboration
between humans and machines.
It's not just that we're building these systems,
but we're also kind of shaping how they think,
how they learn about the world.
Yeah.
It really is this partnership.
And it's a partnership that's always evolving.
As the AI gets more advanced, our role changes.
So it's less about programming and more about teaching
and mentoring.
It's like we're raising a generation of AI kids
and guiding them as they explore this world of information.
That's a great analogy.
And just like kids, there are going
to be challenges and rewards along the way.
So yeah, there are going to be times when the AI makes
mistakes, but there will also be these really incredible moments
of insight and discovery.
The important thing is to approach it
with the balance of excitement, but also some caution.
Making sure we're using it in a way that's good for everybody.
Well said.
So I think as we wrap up this deep dive into this FCTS
grounding leaderboard, this quest for factual AI,
I think the biggest takeaway is that AI and truth,
it's not just a technical problem.
It's really a human one.
It takes this collaboration, this critical thinking.
It's about building a future where AI and human intelligence
can actually work together to help us better understand
the world.
So with that, we want to thank you all
for joining us on this exploration.
We really hope you enjoyed it.
Yeah, thanks for listening.
And keep digging deeper.
Keep asking questions, have these conversations
about the role of OrchieTac in our lives.
And until next time, stay curious.
And remember, the truth is out there.