All right, so you send over this fascinating paper.
It's called Long Bench V2.
And it's about testing these large language models,
or LLMs, can they understand complex text?
Yeah, and what's interesting about this paper,
it goes beyond, you know those simple tests
where AI just has to find a specific answer?
Like Long Bench V2, it's designed to be a lot harder.
Like giving these models a graduate level exam,
can they really understand?
So instead of just like skimming a news article,
it's tackling like a legal document, like a dense textbook.
Exactly, and the lengths they're testing are mind blowing.
It's anywhere from 8,000 words,
Wow.
All the way up to 2 million words.
That's longer than like most books out there.
It's longer than most novels, yeah.
Yeah, why is the length of the text so important though?
Well, think about it this way,
reading a short email, one thing.
Right.
Comprehending a full length novel, different level.
You have to keep track of characters, plot twists,
you know, remember things from chapters ago.
Right.
It's a real test of reading comprehension.
So Long Bench V2 is basically seeing if AI can handle
that kind of like, you know, mental marathon.
That's a good way to put it, yeah.
And not just spit back fat.
Exactly, they wanna see if AI can actually learn
and reason from what it reads.
Can it understand the big picture,
not just the individual pieces?
So how did they actually create this benchmark?
Did they just like, you know, lock a bunch of researchers
in a room and force them to write like super long essays?
Not quite, they actually used 97 highly-educated
annotators from like top universities.
97.
And these annotators, they used real documents
they were already familiar with.
So academic papers, legal documents,
financial reports, even detective novels.
Wow, so 97 annotators.
Yeah.
That's a lot of brain power going into it.
Like what were they doing with all these documents?
They were crafting these really challenging
multiple choice questions.
Okay.
To test deep understanding.
And they made sure that these questions weren't too easy,
so they had three different LLMs try them out first.
So they were using AI to make sure the questions
were tough enough for other AI.
Right, and if all three AI models could answer
a question easily, they threw it out.
Then they had human experts try these same questions.
Okay.
And the experts only averaged about 53.7% accuracy.
Wait, so even humans were struggling with these questions?
Yeah, and keep in mind,
they gave the human experts a 15 minute time limit
for each question.
Okay.
So some of these questions probably require like
hours of reading to fully grasp the material.
This benchmark sounds incredibly tough.
It is.
Okay, so beyond just being long,
what makes these tasks so challenging?
Wow.
What kinds of questions are we talking about?
They cover a wide range of tasks
from analyzing financial reports to figuring out
who doon it in a detective novel.
Six main categories with 20 sub tasks.
So it's a really comprehensive test.
Give me some specific examples.
Like what are these AI models up against?
Okay, so one task is like
single document question answering.
So for example, the AI might have to analyze
a financial report,
answer questions about the company's performance.
But it's not just finding a specific number.
It's understanding the trends,
drawing conclusions from the data.
So it's like asking the AI to be a financial analyst.
Right.
That's impressive.
Then there's multi-document question answering
where the AI has to synthesize information
from multiple sources like news articles
or research papers.
For example, it might have to piece together
a timeline of events based on different accounts.
So like a detective piecing together clues
from like, you know, different witnesses.
Exactly. And then there's long in context learning.
Okay.
The AI has to learn from a lengthy document,
like a user manual.
Okay.
And then answer questions about how to use
like a specific device or software.
Okay, so it's not just understanding what it already knows,
but learning new things on the fly.
Exactly.
Okay, so we've got financial reports.
Detective novels, user manuals.
What else?
They even have tasks focused on understanding
long dialogue histories.
So the AI has to follow conversations
between multiple people.
Okay.
And answer questions about like, what went down?
Imagine like trying to keep up with a group chat.
Yeah.
Dozens of messages.
That's what the AI is dealing with.
That's like the AI is eavesdropping
on a really complex conversation.
Right.
Trying to like figure out who said what
and what they really meant.
Uh-huh.
It's almost like testing its social intelligence.
Yeah.
In a way.
And of course there are tasks focused on code.
Yeah.
So the AI has to understand how a piece of code works,
answer questions about its functionality.
Okay.
This is really challenging because the code
could be really dense and abstract.
Okay.
So I've got finance, detective work,
user manuals, conversations, and even code.
Yeah.
This long bench V2 is a real multifaceted challenge.
It is, yeah.
Now for the big question,
how did the AI actually perform against this benchmark?
Well, remember those human experts?
Yeah.
They averaged 53.7%.
The best performing AI model,
they only got 50.1% right.
So not exactly acing the test?
Not quite, but it's important to note
that this model did perform
significantly better than smaller models.
Yeah.
They only achieved around 30% accuracy.
Okay.
So this suggests that size does matter,
at least for these complex tasks.
Makes sense.
Like a bigger brain can hold more information, right?
Right, exactly.
Didn't you mention earlier though
that one model actually scored higher?
Did any of the AI models outperform humans?
Well, this is where it gets interesting.
Okay.
The researchers also tested a technique called O1 Preview,
and it essentially involves giving the AI model
more time to think and reason through the problem.
So instead of just spinning out a quick answer,
they're like, show your work.
Exactly, like giving the AI scratch paper
to work out the problem.
And with this extra reasoning time,
the O1 Preview model actually achieved 57.7% accuracy.
Wow.
Exceeding the human baseline.
So it seems like giving AI a chance
to really think things through,
that makes a big difference.
Yeah.
What does that tell us about
how AI processes information?
Hmm.
Well, it suggests that AI might not be understanding text
in the same way that humans do.
It seems like incorporating more of the
step-by-step reasoning processes
allows them to better grasp the meaning,
come up with better conclusions.
That's fascinating.
Yeah.
So size matters, reasoning helps,
but even the best AI isn't quite human level yet.
Right.
What else did they discover?
Well, one interesting finding was that the AI models
really struggled with understanding long-structured data.
Huh, that's surprising.
I would have thought AI would be great at, like,
you know, crunching numbers, analyzing data.
You'd think so, right?
Yeah.
But they think it's because these models
haven't been trained as much on structured data,
especially in these long-form contexts.
Okay.
They've been exposed to a lot more text.
So it's back to the idea that, like,
their understanding is only as good
as the data they've been fed.
If they haven't seen a lot of structured data.
Exactly.
There's no surprise they struggle with it.
What about giving them more information to work with, though?
They actually tested that.
They tested something called
retrieval augmented generation, or RAGI, for short.
It's basically, like, giving the AI access to a search engine.
Oh, so AI with Google.
Yeah, pretty much.
And the results were interesting.
Some models, like Quinn 2.5, they did improve,
but only up to a certain point.
Oh, so, like, information overload?
Yeah, kind of.
Ref for AI.
It seems so.
However, there was one model, GPT-4O,
that could effectively use these longer,
retrieved contexts.
Wow.
So this suggests that some models are better
at sifting through tons of information
and picking out what's important.
Okay, one last question before we move on.
How do we know that the AI wasn't just, like,
memorizing answers from its training data?
Like, could it be cheating somehow?
Yeah, that's a great question.
And they were careful to address that.
To check for memorization,
they tested the models
without giving them the long text at all.
Right.
Just the questions, the results.
Pretty much random.
Wow, so this is genuine comprehension.
It's not just, like, AI regurgitating facts.
Exactly.
This is what makes Long Bench V2 so valuable.
It's pushing the boundaries
of how we evaluate AI understanding.
It's forcing us to think differently
about how we train and develop these systems.
It sounds like this research is gonna have
a huge impact on the AI field.
Yeah.
Like, it's already...
It's already sparking new ideas and approaches.
Well, I can't wait to, like,
dive deeper into these implications
and explore, like, what the future holds.
Me too.
There's a lot more to unpack here.
It's really fascinating, like, to see, you know,
how these models are being challenged
and where they still need to improve.
So let's talk about the big picture here.
What does Long Bench V2 tell us about the future of AI?
Are we, like, you know, on the verge
of having, like, AI lawyers or novelists?
Well, not quite yet.
While, you know, we are seeing
these incredible advancements in AI,
Long Bench V2, it highlights
that there's still a lot of work to do.
So is it, like, back to the drawing board for researchers,
are we headed in the wrong direction?
No, not at all.
Long Bench V2, it's not about showcasing AI's failures.
It's about providing a roadmap.
Okay.
It gives researchers this new tool
to evaluate their models, identify weak spots,
and develop new techniques.
So instead of a roadblock, it's more like, you know,
a really challenging obstacle course.
It helps push AI development forward.
Exactly, yeah.
And by, like, studying how different models
handle these challenges, we can learn a lot about
how they learn, how they reason,
how they interact with information.
You mentioned earlier that AI models
struggle with structured data.
Like, is that something researchers are, like,
actively trying to address?
Yeah, absolutely.
There's a growing focus on developing AI
that can handle that effectively.
We might see new training methods, specialized architectures,
or even new ways of representing information.
So we're not just talking about
expanding their training data sets.
We're talking about fundamentally changing,
like how AI processes information.
Yeah, it's not just about teaching AI what to think,
but how to think.
And we're learning that the way AI thinks,
it might be fundamentally different
from how humans think.
That's a mind-blowing thought.
And the researchers specifically called for more research
into scaling inference time compute.
What does that even mean?
Good question.
It basically means giving AI more processing power,
more time to think things through.
Right, so, like, you know,
give them a chance to show their work.
Exactly, yeah.
And this has some interesting implications
for the future of, like, AI, hardware, and infrastructure.
If we want to unlock AI's full potential,
we might need to develop specialized processors,
computing systems, designed for these reasoning tasks.
Wow, so this paper,
it's not just impacting, like, you know, the AI algorithms.
It's influencing the development of the hardware
that powers those algorithms.
Right.
It's like a ripple effect.
What about the software side of things?
Yeah, that's another crucial aspect.
As we build AI that can handle massive amounts of data,
we need to develop better tools and interfaces
so that humans can interact with them effectively.
We need to understand how their reasoning
guide their learning and ensure they're used responsibly.
So we need a translator, right?
Like, between human thinking and AI thinking.
Exactly.
And this is where the field of human-computer interaction
becomes important.
This is all incredibly exciting,
but are there any, like, potential downsides,
concerns we should be aware of?
Of course, like with any powerful technology,
there are risks and challenges.
We need to ensure that AI is developed and used ethically
in a way that benefits society as a whole.
Absolutely.
But, like, did the researchers identify any limitations of?
Like, long bench V2 itself?
They did.
They mentioned a few.
One is the size of the benchmark.
Okay.
With only 503 questions,
it's not as comprehensive as some other benchmarks.
The results might be more sensitive to randomness
and may not fully represent AI's capabilities.
So, like, a larger, more diverse benchmark
would give us a more accurate picture.
Exactly.
They also acknowledge that the current data set
is limited to English.
Obviously, this doesn't reflect the global nature of language.
So, expanding to other languages
is a major challenge for future research.
That makes sense.
Like, translating those tasks and making sure
they're equally challenging.
That's no easy feat.
And, you know, language is so nuanced.
Yeah.
Small differences can change the meaning.
So, developing AI that can truly understand
and reason across languages, it's huge.
It's like we're trying to teach AI,
not just different languages, but different ways of thinking.
Exactly.
Okay, we've covered a lot of ground here.
We've talked about the benchmark, the results,
the impact, the limitations.
Is there anything else we should highlight
before we, you know, move on?
I think it's worth emphasizing that
while Longbench V2 focuses on long-form text,
it also has implications for other areas of AI research.
Okay.
For example, it could inform AI assistants
that understand more complex requests
or maybe help us create educational tools.
So, the insights from this research
could, like, ripple out into different applications.
Absolutely.
Yeah, it's all connected.
The ability to process and understand information,
it's fundamental to so many areas.
This has been, like, an incredible deep dive.
I feel like we've only scratched the surface.
It has been a thought-provoking discussion.
And for those who want to explore further,
the researchers have made Longbench V2 publicly available.
That's great.
Yeah.
It's fantastic that they're promoting, like, you know,
open science and encouraging collaboration.
And then science is really important, yeah.
But before we sign off, is there one final thought,
something that, like, wasn't explicitly mentioned in the paper
that you think our listeners should, like, ponder?
Let me see.
You know, we've talked a lot about the technical aspects,
how well it can understand information.
But I think it's important to remember
that AI is still a tool.
OK.
It can be used for incredible things,
but it's only as good as the humans who are developing it,
using it.
That's a great point.
We can't just, like, blindly trust AI to solve all our problems.
We need to be thoughtful about, like, how we design it.
Right.
How we train it.
Yeah.
How we use it.
We need to be aware of its limitations,
its potential biases, its impact on society.
Hmm.
We need to be asking the tough questions
and making sure that AI is aligned with our values
and our goals.
Absolutely.
We have a responsibility to steer AI in the right direction,
use it to make the world a better place.
Well said.
I think that's a perfect note to end on.
This has been a truly mind-expanding deep dive
into, you know, Longbench V2 and the future of AI.
It has been fun.
A huge thanks to you for, like, you know,
sharing your expertise and insights.
It really has been fascinating.
And I think, you know, our listeners
will be buzzing with all these new ideas and questions, too.
I hope so.
And for those, you know, who are eager to kind of dive
even deeper, the researchers made Longbench V2
publicly available.
That's fantastic.
Anyone can, you know, explore the data,
see how different models perform.
This kind of open access, it's so important
because it allows researchers around the world
to collaborate, compare results, and contribute to, like,
you know, the progress
of the field.
So it's not just about, like, one team racing
to the finish line.
It's about a global community working together.
Exactly.
And by sharing these resources, we
can ensure that AI development, it's
guided by ethical considerations,
benefits humanity as a whole, and it doesn't become something
that's just controlled by, like, you know,
a few powerful entities.
That's a great point.
It's about, like, democratizing AI
and making sure that it's used for good.
It benefits everyone, not just, like, a select few.
Right, right.
OK, before we wrap up this deep dive,
is there, like, one final takeaway,
something you think our listeners should really ponder?
Hmm, let me see.
You know, we've talked a lot about, like,
the technical aspects of AI, how well it
can understand information.
But I think it's important to remember
that AI is still a tool.
You know, it can be used for incredible things,
but it's only as good as the humans who are developing it
and using it.
That's a great point.
Yeah.
We can't just, like, blindly trust AI
to solve all our problems.
We need to be thoughtful about, like, how we design it,
how we train it, how we use it.
We need to be aware of the limitations, potential biases,
impact on society.
Right, we need to be asking, like, the tough questions
and making sure that AI is, like, you know,
aligned with our values and our goals.
Absolutely, yeah.
We have a responsibility to steer AI in the right direction,
to use it to make the world a better place.
Well said.
I think that's, like, a perfect note to end on.
This has been a truly mind-expanding deep dive
into the world of Longbench V2 and, you know,
the future of AI.
A huge thank you to you for, like, sharing
your expertise and insights with us.
It's always a pleasure to dive into these topics,
and I can't wait to see what breakthroughs emerge
in the coming years.
To you, our listeners out there.
Keep those brains buzzing.
We'll be back soon with another deep dive
into the latest developments in AI.
Until then, happy learning.