Hey everyone, and welcome back for another deep dive with us.
Today we're gonna be looking at a pretty cool research paper,
really digs into some of the assumptions
that we've been making about these large language models
or LLMs.
Yeah, you know, all this hype around chat GPT
and its amazing abilities.
Well, this paper kind of throws a wrench in the gears
and asks, are we maybe getting a little ahead of ourselves
when we talk about how good these LLMs are at reasoning?
Right, so like, we always hear about how these LLMs
are acing these benchmarks and tests,
but this paper is like, hmm,
are those tests really giving us the full picture?
Yeah, and that's where this whole idea of pass at K comes in.
It's this metric that a lot of researchers have been using
to basically see if an LLM can solve a problem
within a certain number of tries.
But the paper argues that just because an LLM
can eventually solve a problem,
doesn't actually mean it really gets it.
You know what I mean?
It's like passing a test after you've taken it like five times.
You might get there eventually.
But does that really mean you've mastered the material?
Exactly, so they introduced this new metric
called G pass at K,
which focuses on both peak performance and consistency.
Can the LLMs solve the problem not just once,
but like every time reliably?
Ooh, okay, so G pass at K is all about raising the bar.
No more flukes, no more lucky guesses.
Right, it's about seeing if these LLMs
can actually demonstrate real understanding.
So how did they put this G pass at K to the test?
Did they like create some sort of AI obstacle course?
Well, they came up with this really clever benchmark
called Live Math Bench,
and it's specifically designed to challenge LLMs
with some seriously tough math problems.
Oh wow, so we're not talking
about your average high school algebra here.
No, we're talking Olympiad level stuff,
problems that would make most people's heads spin.
And I'm guessing they got rid
of those multiple choice questions too.
Absolutely, it's all about forcing these LLMs
to come up with the solutions themselves.
So no more process of elimination,
just straight up problem solving skills.
Exactly, and the results were pretty eye-opening.
I bet.
So what happened when these supposedly brilliant LLMs,
the ones acing all the other tests,
what happened when they faced Live Math Bench
and this whole G pass at K scrutiny?
Their performance tanked.
We saw drops of like 50%, sometimes even 90%,
compared to their scores on those single attempt tests.
Wow, that's a huge difference.
So it seems like just making these LLMs bigger
and feeding them more data,
doesn't actually guarantee better reasoning or consistency?
Yeah, it's a bit of a reality check, you know.
Bigger isn't always better when it comes to AI.
And what about those fancy 01-like LLMs,
the ones everyone's so excited about
because they can kind of reason step by step like us?
Do they hold up any better?
Well, even they showed some inconsistencies
when the problems got really complex.
It seems like even with those step by step approaches,
there's still room for improvement.
Okay, so before we get too deep into that,
could you maybe just back up for a second,
explain what these 01-like LLMs are.
For those of us who haven't been following
every twist and turn in the AI world.
Sure, so these 01-like LLMs are kind of a new breed.
They're designed to try and mimic human thinking
a little more closely.
Instead of just spitting out an answer,
they can actually show their work,
breaking down a problem step by step,
just like a human would.
Oh, that's interesting.
So it's all about transparency.
We can actually see how the LLM is arriving
at its conclusion.
Exactly, and this was seen as a big step
towards creating more reliable and trustworthy AI.
Makes sense.
But if even these 01-like LLMs
are struggling with consistency,
it makes you wonder if we've been overestimating
how close we are to true AGI.
Yeah, it definitely raises some important questions
about what it truly means for an AI to understand something
and how we can actually measure their reasoning abilities
in a way that's meaningful.
Lots to ponder there for sure.
Well, we're gonna take a quick break
and then come back to unpack some of those deeper questions.
Sounds good, I'm ready to dive in.
All right, so we're back.
And before the break,
we're talking about those 01-like LLMs.
You know the ones that try to reason step by step.
All right, like they're trying to show their work,
so to speak, instead of just giving us the answer.
Exactly, and that was seen as a really promising step
towards more transparent and reliable AI.
But even those 01-like LLMs were showing some hiccups
when faced with those really challenging math problems.
Yeah, and that kind of makes you wonder,
are we maybe getting a little carried away
with all this talk about AI reaching human level intelligence?
It's a big question, isn't it?
I mean, if true AGI artificial general intelligence
is about creating AI that can really think like a human,
you know, learn, adapt, solve problems
in all sorts of different situations,
then this research suggests
that we might still have a ways to go.
So we're not quite at the point
where AI can truly understand.
It's more like they're really good at spotting patterns.
That seems to be the case for now.
LLMs are amazing at finding connections
in massive amounts of data.
But true reasoning goes beyond just recognizing patterns.
It's about grasping those underlying concepts,
applying logic to new situations,
even when things get a bit messy.
Right, it's like knowing the rules of a game
versus actually being able to strategize.
You might be able to follow the steps,
but that doesn't mean you're a master.
Exactly, and that's where LLMs seem to hit a bit of a wall.
They can follow the rules,
they can excel in certain scenarios,
but when faced with something truly novel or ambiguous,
that's when those inconsistencies start to creep in.
So does this mean that the dream of AGI is dead?
Should we just pack it up and call it a day?
Not at all.
This research is actually really valuable
because it helps us understand where we need to improve.
It's like a roadmap highlighting the areas
where LLMs need to evolve.
So instead of being discouraged,
researchers are using these findings to push forward.
Exactly, they're exploring new approaches,
refining their techniques,
trying to close that gap between pattern recognition
and true understanding.
So what are some of the most promising directions?
What are researchers focusing on
to make these LLMs more robust?
Well, one really interesting area
is incorporating more structured knowledge.
Instead of just feeding LLMs tons and tons of text data,
they're experimenting with giving them
more formal definitions, theorems, proofs,
almost like giving them a solid foundation
in the subject matter.
So it's not just about showing them the problems,
it's about giving them the tools
to understand the underlying principles.
Right, it's like giving them a textbook
alongside the problem set.
The hope is that by equipping LLMs
with this more structured understanding,
they'll develop more consistent
and reliable reasoning abilities.
That makes a lot of sense.
So what other approaches are showing promise?
Another interesting area is focusing on training methods
that really emphasize step-by-step reasoning.
Remember those O1-like models we were talking about?
While they still have limitations,
their ability to break down problems step-by-step
is a step in the right direction.
So it's about understanding the process
of getting to the answer.
Not just the answer itself.
Exactly, it's about valuing the journey
as much as the destination.
It's about trying to understand
how humans think and reason,
and then translating those insights
into more effective AI training methods.
It sounds like the field is constantly evolving,
always pushing the boundaries of what's possible.
But for those of us who maybe aren't deep
in the technical weeds,
what are some of the key takeaways from this research?
Well, first of all, don't be swayed by all the hype.
You know all those headlines about LLMs
achieving human-level performance?
Always dig a little deeper, ask critical question,
and try to understand
how those claims are actually being measured.
Right, it's like with anything else,
you gotta read the fine print.
Exactly.
And second, remember that while LLMs
are incredibly powerful,
they're still primarily pattern matchers at this point.
They can be amazing tools,
but we need to use them responsibly,
understanding both their capabilities and their limitations.
So excitement, yes, but also a healthy dose of realism.
Exactly.
And finally remember that the pursuit of true AGI
is a marathon, not a sprint.
There will be hurdles, there will be surprises,
but each step we take brings us closer
to unlocking the full potential of AI.
And who knows, maybe this research,
despite highlighting the current limitations of LLMs,
might actually be a turning point,
a moment that sparks new innovation
and sets us on a more solid path towards AGI.
It definitely has the potential to be a game changer.
And speaking of game changers,
in the final part of our deep dive,
we're gonna explore some even bigger questions
and challenge our listeners to think even more deeply
about the future of AI and what it means for all of us.
Welcome back everyone for the final part of our deep dive.
We've covered a lot of ground today.
You know from those potentially misleading metrics
we've been using to what this all means
for the quest for true artificial general intelligence.
But as we wrap things up,
we kinda wanted to shift gears a bit.
Yeah.
And throw it back to you, our listeners.
Yeah, because at the end of the day,
this research isn't just about technical benchmarks
and fancy algorithms.
It's about sparking a broader conversation
about the future of AI
and how it's gonna impact all of our lives.
So we have a little challenge for you.
Go check out the full research paper yourself.
We'll be sure to include a link in the show notes.
Yeah, dive into those details.
Explore this whole G-Pass IKEA metric,
the one that really emphasizes consistency
alongside peak performance.
And think about how it could be applied
beyond just math problems.
You know, we touched on a few possibilities earlier,
like healthcare finance, even creative fields
like art and music.
But really the potential applications
are pretty much endless.
Imagine a world where we could evaluate
the consistency and reliability of AI in almost any domain.
That would be huge.
It could really change the game
when it comes to building trust in these systems.
Right, making sure that they're not
just making lucky guesses,
but actually reasoning their way through complex problems.
It won't be intimidated by the technical stuff.
This research raises some fundamental questions
that anyone can grapple with.
Like, what does it really mean
for an AI to understand something?
How can we measure their reasoning abilities
in a way that truly makes sense?
And what are the ethical implications of all this?
As we create AI systems that are more and more powerful.
These are questions we should all be thinking about.
Especially as AI continues to advance at such a rapid pace.
So we encourage you to dig into this research,
share your thoughts, join the conversation.
Because the future of AI isn't something
that's just happening to us.
It's something we're all shaping together.
And on that note, we'll wrap up this deep dive.
Thanks for joining us on this fascinating exploration.
And we'll see you next time for another journey
into the world of AI.