ever get like bad info from an AI.
You're like, wait a minute, how could it be so wrong?
Like these AIs can just hallucinate facts.
And it can be kind of scary,
especially as we use AI more and more for important stuff.
Yeah, absolutely.
And that's what's so interesting about this research
we're gonna deep dive into today.
It's all about measuring how well AI
can tell fact from fiction.
Okay, so like separating the real from the made up
in the AI world.
What's the approach here?
How did they try to figure that out?
Well, researchers at OpenAI actually created
this new benchmark, they call it SimpleQA.
And it's basically a giant test for AI models.
Like think of it as a trivia game,
but specifically designed to test how good AI is
at handling straightforward factual questions.
Ah, so it's not about how fancy the AI can write
or code or anything like that.
It's about whether it actually knows stuff
about the real world.
Exactly, it's all about factual accuracy.
Interesting, so tell me,
how does this SimpleQA thing actually work?
Okay, so imagine like a massive trivia game,
but for AI, right?
SimpleQA has over 4,000 of these short questions.
And each question has a single correct answer.
No room for interpretation.
Oh, so like true or false?
But for AI, no wishy-washy answer is allowed.
Yep, it's about as straightforward as it gets.
And the questions they came up with
are surprisingly tricky.
Things like who won the Nobel Prize in Physics in 2022?
Or what's the capital of Australia?
Okay, yeah, those sound pretty simple.
I feel like I should know those.
But I'm guessing they made these questions
a lot tougher than they sound.
Oh yeah, they definitely didn't go easy on the AI's.
They actually designed the questions to be hard,
even for really advanced models like GPT-4.
Wow, so they were really trying to push these AI's
to their limits.
I mean, how did they even come up with questions
that could stump something as smart as GPT-4?
Well, they used this process called adversarial collection,
which basically means they kept tweaking
and refining the questions until even the most advanced
models were struggling to get them right.
So they were determined to find those AI weak spots.
Clever, but okay.
So let's say they've got all these tricky trivia questions
for the AI.
How do they actually know if the AI
is getting the answers right?
Like how do they make sure they're not grading the AI
based on bad information?
That's a really good point.
Data quality is super important.
And the researchers knew that.
So they were incredibly meticulous
about how they made sure the answers themselves were correct.
They used two separate AI trainers
to answer each question independently.
And they only kept the questions
where both AIs gave the same answer.
Oh, so it's like double checking their work.
It's like, okay, you two AIs agree on this
so we can be pretty confident this answer's right?
Yeah, and not only that, but they also insisted
that every single answer had to be backed up
with solid evidence from a reputable source.
So it's like showing your work in math class.
You gotta be able to prove where you got that answer from.
Exactly.
It can't just be like some random guess.
It has to be verifiable.
I like that.
Makes a lot of sense.
So besides this whole double checking thing,
what were some of the other criteria they used
to pick which questions to include?
Like, were there any rules about the kind of questions
that made it into simple QA?
Oh yeah, for sure.
They had a few really important rules
for selecting the questions.
First, the question had to have one and only one right answer.
Like a hard fact that doesn't change over time.
Okay, so no trip questions or opinions,
just straight up undeniable truths.
Yep.
And then they also made sure that all the questions
can be answered using information that was available
up to the end of 2023.
So nothing super recent,
that the AI might not have had a chance to learn yet.
Oh, that makes sense.
You don't wanna test the AI on something
it couldn't have possibly known.
Right, and of course the answer had to be provable
with evidence, we already talked about that.
Right, right.
So verifiable facts, no recent events, no trickery.
Okay, got it.
Can you give me some actual examples
of the kinds of questions they ask in simple QA?
I'm really curious now.
Absolutely.
Let's see, so table one in their paper
has some good examples.
Like one question is,
who received the IE Frank Rosenblatt Award in 2010?
Okay, I'm guessing I'm supposed to know that.
Well, the answer is Michio Sugino.
Another one is, on which US TV station
did the Canadian reality series to serve and protect debut?
Oh wow, see that's way too specific for me, I have no idea.
And the answer to that one is KVOS TV.
Okay, so they're not messing around with these questions.
They really span a pretty wide range of topics too.
It sounds like they were trying to make sure
the AIs had to know a little bit of everything,
like science, history, pop culture.
Did they mention if they focused
on any specific subject areas?
Not really, they were really going
for breadth of knowledge with this.
Like they wanted to make sure these AIs
had a good understanding of facts
across a whole bunch of different areas.
It's kind of like Jeopardy, but for AI.
I like it, an AI game show.
That'd be fun to watch.
Where do they even get all this information
from, all these facts and figures?
They used a variety of sources,
but they tried to stick to really reliable ones.
So Whippypedia was their go-to.
But they also pulled from things like fandom websites
and academic articles, even IMDB
for some of the movie and TV stuff.
Right, so they weren't just like Googling random trivia.
They were actually trying to make sure
the AI was learning from trustworthy sources.
Yeah, exactly.
So we've got these super tricky trivia questions,
all fact checked and everything.
But how did they actually grade the AI's answers?
Was it just like right or wrong, or was there more to it?
They actually used three different grades.
So you had correct, incorrect, and then not attempted.
Not attempted.
That's interesting.
Why would an AI not even try to answer a question?
Well, sometimes an AI might recognize
that it just doesn't know the answer.
And it doesn't want to just guess randomly.
So it might say something like,
I don't know or I need more information.
And in those cases, they would mark it as not attempted.
So a little bit of AI humility there.
Probably a good thing.
Yeah.
Right.
So what are the examples of how the grading worked
for the correct and incorrect answers?
Sure.
So Table Two in the paper has some good examples.
Let's say there's a question about the Dutch player
who scored in the 2022 World Cup game against Argentina.
The correct answer is Wout Weghorst.
So any response that includes his full name,
even if it adds some extra details about the game,
would be marked as correct.
Okay, so as long as the AI gets that key fact, right?
It's good.
What about an incorrect example?
Okay, so for that same question,
if the AI answered Virgil van Dijk,
or even if it said like Virgil van Dijk and Wout Weghorst,
it would be considered incorrect
because it's contradicting that single correct answer.
They're really strict about there only being
one verifiable answer.
Got it.
No partial credit here.
This all sounds super labor intensive.
Did they have actual humans grading
all of these AI responses?
Actually, they use AI to grade the other AI.
Whoa, AI inception, I like it.
Yeah, they created the special chat GPD classifier.
And gave it detailed instructions
on how to grade the answers.
They even gave it examples for each category.
So it knew exactly what to look for.
So meta and AI grading other AI,
did they have any humans involved in this at all though?
Oh yeah, for sure.
Humans were still definitely part of the process.
So they had a third AI trainer
answer a random sample of 1,000 questions
just to kind of double check the accuracy
of the whole benchmark itself.
And then they also had humans review any questions
where the AI graders disagreed,
or if the AI flagged any potential problems.
So there was still that human oversight
to make sure everything was running smoothly.
Exactly, it wasn't just AI's gone wild.
Good to know.
Did they say anything about how accurate
they think the benchmark itself is?
Like what's the margin of error here?
They estimated that the benchmark has
about a 3% error rate, which is actually really impressive.
When you consider how big and complex this whole project was,
they did find that some of the questions
were maybe a little bit ambiguous,
or that even some reputable sources
would sometimes have conflicting information.
Right, it happens.
Even human experts disagree sometimes.
So they've got this massive data set
of challenging trivia questions for AI,
all graded and double checked.
What did they actually do with all this?
How did they use this simple QA thing
to test different AI models?
So they basically used simple QA
to test a whole bunch of different AI models,
kind of like an AI Olympics, you know?
See how they all stacked up against each other.
Oh, I like that.
So it's a benchmark.
To see which AIs are the trivia champs,
did any of them get a perfect score?
Not even close.
Even the most advanced models, like GPT-4,
they still struggled with some of the questions.
So no AI geniuses just yet.
Makes you wonder how hard it really is to build an AI
that can actually understand and remember facts
the way humans do.
Yeah, it's a really tough challenge.
And this research definitely highlights that.
So how did the different models actually perform?
Did they notice any interesting patterns or differences
between how the different AIs handled the questions?
Yeah, definitely.
So they tested a bunch of different versions of GPT-4,
and then they also tested this model called Clawd,
which is made by a company called Anthropic.
And one thing they found was that generally
the bigger, more complex models did better on simple QA.
Okay, that makes sense.
The more parameters, the more data.
The smarter the AI is gonna be.
But were there any surprises,
any models that did better or worse than you might expect?
There were, actually.
So one interesting thing they found was that the Clawd models
were a lot more cautious than the GPT-4 models.
Like they were way more likely to say,
I don't know, rather than risk getting the answer wrong.
Oh, so it's like Clawd was playing it safe
while GPT-4 was more willing to take a gamble,
even if it meant making a mistake.
That's kind of funny when you think about it,
like different AI personalities.
Did they have a term for this cautiousness?
Yeah, they called it calibration.
So in this context, calibration basically means
how well an AI can judge its own likelihood
of being right or wrong.
Kind of like knowing when to bet big
and when to fold in poker.
Ah, so a well-calibrated AI knows its limits.
While an overconfident AI might just bluff its way through
even when it has no clue.
Interesting.
So how did they measure this calibration thing?
Well, they actually used two different methods.
So first, they straight up asked the AI
how confident it was in its answer.
Like they would use prompts like,
on a scale of zero to 100%,
how sure are you about this answer?
So they were making the AI rate
its own confidence level.
That's pretty clever.
What did they find?
Were any of the AIs actually good
at judging their own knowledge?
Well, it turns out most models,
even the really advanced ones,
tend to be a bit overconfident.
Like they often think they know more than they actually do.
Classic case of AI hubris.
But hey, at least they're optimistic.
What was the second way they measured calibration?
Okay, so for this one, they got a little creative.
They asked the same question to each AI model 100 times,
but with little variations
in how the question was phrased each time.
Oh, so they were trying to see
if the AI would give the same answer consistently.
Even when the wording of the question was slightly different.
Exactly, because the idea is that
if an AI keeps giving the same answer,
even when the question changes a little bit,
it's probably more confident in that answer.
Right, so they looked at how often
each AI gave consistent answers
and compared that to how accurate it actually was.
So if an AI gave the same answer,
like 80 times out of 100,
you'd want to see it getting at least 80% of those answers.
Right, to consider it well calibrated, makes sense.
What did they find when they did this experiment?
So it turns out that accuracy did generally go up
when the AI was giving more consistent answers,
which is a good sign.
And there was this one model,
01 Preview from OpenAI,
that really stood out as being particularly well calibrated.
Like its answer consistency
pretty much matched up with this actual accuracy.
Oh, so 01 Preview had a good sense
of when it knew something for sure.
And when it was just guessing,
did that mean it aced the whole simple QA test though?
Not necessarily, remember calibration
is just one piece of the puzzle.
It doesn't guarantee that an AI
will get every answer right.
Right, it's like saying someone is really good
at knowing what they don't know.
But that doesn't mean they're a genius.
They still need to have the actual knowledge to back it up.
Did they look at any other aspects
of AI factuality in this research?
They did, actually.
One of the most interesting parts of this research
was not just measuring AI factuality,
but also starting to really understand its limitations.
And that's something we can dig into
a little deeper in the last part of our deep dive.
All right, so we've talked about
how this simple QA thing works
and how they grade the AI's answers.
And even how they measure whether an AI knows when
it's bluffing or not.
But you mentioned there's some limits to all this.
What are some of the things that simple QA
doesn't really tell us about AI in facts?
Yeah, that's a great point.
I mean, simple QA is a great starting point,
but it definitely has its boundaries.
It's important to remember that it was really designed
to zero in on those short factual questions
with one clear right answer,
kind of like a trivia game for AI.
Right, very controlled, very specific.
But what's the problem with that?
Isn't that a good way to start understanding
how AI handles facts?
It is, for sure.
But we also have to keep in mind
that the real world is a lot messier
and a lot more complex than a trivia game.
The researchers themselves even point out
that just because an AI can answer these trivia questions
doesn't necessarily mean it can handle
like longer, more nuanced tasks,
like writing a factual news article
or summarizing a complicated research paper.
So simple QA is kind of like a basic skills test.
You have to pass it before you can move on
to the harder stuff, but acing the basics
doesn't mean you're gonna be a star performer
in the real world.
Exactly, and even if an AI does really well on simple QA,
it doesn't mean that it's like totally immune
to making things up.
It just means it's less likely to get things wrong
in this very specific context.
Right.
Got it.
So we can't just assume an AI is like a perfectly factual
oracle based on how it does on one test.
We need to keep coming up with new ways to evaluate
and improve AI's grasp of facts
across all sorts of different tasks and complexities.
Where do you think research in this area should go next?
What are some of the big questions we still need to answer?
That's the million dollar question.
I think one of the biggest challenges
is figuring out how to evaluate AI factuality
in more open-ended situations.
Like how do you measure accuracy
when there isn't just one single right answer?
Think about summarizing a long document or writing an essay.
Can we develop benchmarks for those kinds of tasks?
Yeah, that sounds really tough.
It's like going from multiple choice to essay questions,
way harder to grade.
Any other areas you think are important for future research?
Definitely.
I think we also need to understand
why these AI systems sometimes make stuff up.
Like what's going on under the hood?
Is it because of how they're trained?
Or is there something more fundamental
about how they process information
that makes them prone to these errors?
If we can answer those questions,
I think we'll be a lot closer to developing AI systems
that are truly reliable and trustworthy.
Yeah, makes sense.
If you know why something breaks,
you're more likely to be able to fix it.
Well, this has been a fascinating deep dive
into the world of AI and facts.
Really appreciate your insights on this.
My pleasure.
Any final thoughts for our listeners before we wrap up?
Hmm, I guess the main takeaway is that this whole AI
factuality thing is a really complex issue.
And it's definitely still a work in progress.
We're making some good headway with tools like SimpleQA.
But there's still a lot more to do.
And I think it's important for everyone,
not just AI experts, to understand these limitations
and to be part of the conversation
about how we can make sure AI is used responsibly
and ethically.
You know, it's a big deal.
Absolutely.
Well said.
All right, on that note, we're gonna wrap up
this episode of the deep dive.
Thanks for joining us on this exploration of AI and facts.
And until next time, stay curious.