Hey everyone and welcome back to AI Papers podcast daily.
So today we're gonna be taking a deep dive into a paper
that's tackling a really, really interesting challenge.
How to make AI think more logically.
Right.
You've probably heard all the buzz about LLMs.
Yeah.
Large language models being amazing at like writing stories,
translating languages, all that.
Yeah, they're great at all that stuff.
But logic puzzles, not so much, right?
Yeah, that's right.
LLMs, they often rely on just recognizing patterns
in the data, you know, kind of like finding trends.
But when it comes to actual reasoning, they can hit a wall.
Yeah, and that is where this paper comes in.
Okay.
It introduces this super fascinating concept
called critical questions of thought.
Okay.
Or CQOT for short.
Okay.
And it's all about giving LLMs a structured way
to actually like question their own logic.
Interesting.
So we're looking at the work of Federico Castagna.
Isbel Sassoon and Simon Parsons.
And their paper is called Critical Questions of Thought.
Steering LLM reasoning with argumentative querying.
Catchy title.
I know, right?
Yeah.
So what's really interesting is that they draw inspiration
from argumentation theory.
Mm-hmm.
Like, you know how we build and analyze arguments
like in a debate?
Oh, interesting.
So instead of just like giving an answer,
the AI actually has to justify its reasoning.
Okay.
Like a lawyer presenting a case.
Exactly.
The paper uses this model of argumentation
developed by Stephen Tolman.
Okay.
And Tolman argued that a strong argument
needs more than just a claim.
It needs evidence warrants to connect that evidence
to the claim.
Okay.
Backing to support those warrants.
And even the ability to address rebuttals.
Wow.
So CQOT applies the same rigor to AI.
Yeah.
Essentially forcing it to think through
each step of its logic.
That's so cool.
So how does this actually work like in practice?
Right, we're all.
Give the AI like a textbook on argumentation.
Well, not quite.
The researchers created this pipeline with four key steps.
Okay.
First, the LLM, it creates a reasoning plan for the problem.
Got it.
Brings it down into premises and conclusions,
kind of like outlining an essay before you start writing.
So the AI is saying,
okay, here's how I'm gonna tackle this problem
step by step.
Precisely.
Cool.
Then it gets really interesting in the second step.
Okay.
The AI actually probes its own reasoning.
Really?
Using eight critical questions.
Wow.
Inspired by Tullman's model.
Okay.
And these questions are designed to expose any flaws
in the AI's logic.
So like a self-check to make sure it's not making any leaps
in its thinking.
Exactly.
Like, you know, are the premises supported by evidence?
Uh-huh.
Or does the reasoning avoid logical fallacies?
Right.
And then step three,
the AI actually checks its answers
to those critical questions.
Right.
And if its reasoning didn't quite pass muster,
Yeah.
it goes back to step one,
revises its plan and tries again.
Oh, wow.
It can actually go through this cycle up to 11 times.
That's wild.
So it's like the AI is having an internal debate with itself.
Yeah.
Challenging its own assumptions.
It really is.
Oh, yeah.
And only once it's satisfied with its reasoning
does it move to step four and give its final answer.
Okay.
So by going through this whole process.
Right.
The AI is much more likely to arrive
at a logically sound conclusion.
Yeah.
Even for really tricky problems.
OK.
So we've given the AI this rigorous training in logic.
Right.
But how do we know if it actually works?
That's where the researchers got really creative.
OK.
They tested CQOT on a variety of LLMs,
both open source models like Lama and Nimitron,
and some of the big names in proprietary AI like Gemini,
GPT4O, and Claude.
So a real AI showdown.
Yeah.
It was quite a showdown.
What kind of tasks did they use to test them?
Well, they use a benchmark called MTBench,
which is specifically designed to challenge AI
with these complex reasoning and math problems.
Gotcha.
Think of brain teasers that would make even a sweat a little.
OK.
Yeah.
I'm intrigued.
And the results.
I'm on the edge of my seat here.
We'll get ready for this.
CQOT significantly improved the performance of all these LLMs
across the board.
Really?
And here's the real kicker.
They achieved the best performance in 18 out of 20 test cases.
Wow.
That's impressive.
It is pretty remarkable.
So this CQOT method is really making a difference.
Yeah.
It's making a big difference.
But hold on.
Didn't you say they tested this on both open source
and proprietary models?
I did, yeah.
How did they compare, like head to head?
Oh, that's where it gets even more interesting.
OK.
One of the most remarkable findings
was that the open source LLAMA model, when enhanced with CQOT,
actually outperformed the baseline performance
of the much larger and more complex GPT-4O.
Wait, so a smaller, more accessible model
beat out one of the industry giants?
Yeah, that's right.
That's a game changer.
It's huge.
Wow.
And to really make sure that the critical questions were
the key factor driving this improvement.
Right.
The researchers even did an ablation study.
OK.
Remove the questions from the process.
Yeah.
And guess what?
The performance dropped significantly.
So the questions really are the secret sauce here.
They really are.
We're giving AI the tools to not just solve problems,
but also to think critically about its own solution.
That's exactly right.
This is seriously cool stuff.
It is really cool stuff.
But before we get too carried away,
what about the limitations?
Right.
Is CQOT like a silver bullet?
Well, not quite.
OK.
While CQOT shows this incredible promise,
there are some things to consider.
Even with the sophisticated approach,
AI is still limited by the data it's been trained on.
If it hasn't encountered certain concepts or types
of reasoning before, it's going to struggle.
It's like giving someone the best study guide in the world.
But if they haven't attended class or read the textbook,
they're not going to ace the test.
Right.
It needs that foundational knowledge.
Exactly.
OK.
So that's one thing.
Yeah.
And also, this approach can be more time consuming
than simpler approaches to AI reasoning.
Because the AI is going through these multiple rounds
of self-evaluation and revision, it can take longer
to arrive at a final answer.
So there's a trade-off between speed and accuracy.
Exactly.
And remember, this research was only
tested on a limited set of LLMs.
Right.
So we need more research to see how CQOT performs
on other models, especially smaller ones
with fewer parameters.
That makes sense.
Yeah.
It sounds like there's still a lot to explore.
Oh, absolutely.
This is just the beginning of the journey.
Yeah, just the beginning.
We've covered a lot of ground already.
But before we dive into the deeper implications
of this research, I think it's time for a quick break.
Oh, it's OK.
When we come back, we'll unpack what these findings mean
for the future of AI and how this research could impact
the way we think about building truly intelligent machines.
Stay with us.
All right.
See you in a bit.
Welcome back to AI Papers podcast daily.
Before the break, we were just getting into this,
like, groundbreaking paper on critical questions of thought
or CQOT and how it's kind of pushing
the boundaries of AI reasoning.
Yeah, it's really exciting stuff.
You know, giving LLMs that structured way
to question their own logic.
Yeah.
We're seeing some really significant improvements
in their ability to tackle these complex problems.
Yeah, we talked about the impressive results
that they got on the MT bench.
Right.
But I'm curious, like, what are the implications
of this research for the broader AI landscape?
Like, what does CQOT mean for the future of AI development?
Well, I think one of the most significant implications
is the potential for CQOT to make AI systems more
transparent and trustworthy.
So by forcing LLMs to justify their reasoning step by step,
we can kind of get a glimpse into their thought process.
And that can help us identify potential biases, errors,
or areas where the AI needs additional training.
So it's like having a window into the AI's mind.
Exactly.
Which is crucial for building confidence in its decisions.
Absolutely.
Especially as AI becomes more integrated
into our everyday lives.
Exactly.
That transparency is going to be essential.
Yeah.
Like, we need to be able to understand how AI arrives
at its conclusion, especially when those conclusions have
real-world consequences.
Yeah, especially in fields like health care or finance.
Right, where the stakes are so high.
Exactly where the stakes are high.
OK, so transparency, that's a big one.
Yeah, and another exciting implication, I think,
is the potential for CQOT to actually democratize
access to these advanced AI capabilities.
OK, how so?
Well, remember how we talked about the open source LLMA model?
Right.
When combined with CQOT, actually outperformed
the much larger GPC4O?
Yeah, that was a real shocker.
Yeah, it was surprising.
It suggests that CQOT can unlock the potential of AI models
that are accessible to a wider range of researchers
and developers, not just those with massive resources.
Right, so it could really accelerate innovation
in the field.
Yeah, I get it.
Imagine smaller teams and startups,
what they could achieve with access
to those really powerful reasoning capabilities.
Yeah, that's what's exciting to think about.
And then there's the potential for CQOT
to inspire new ways of thinking about human learning
and reasoning.
Ooh, that's interesting.
Yeah, if AI can benefit from this critical self-questioning,
perhaps we can too.
I love that idea.
Maybe we should all adopt a bit of that CQOT mindset
in our own lives.
Yeah, I think we could all benefit
from being a bit more critical of our own assumptions
and thought processes.
For sure, for sure.
OK, so we've talked a lot about the potential
upsides of CQOT.
Right.
But what about the challenges?
Are there any roadblocks we need to consider?
Well, of course, no technology is without its limitations.
Right.
One challenge is that the CQOT pipeline
can be more time consuming than simpler approaches
to AI reasoning.
OK.
Because the AI is going through those multiple rounds
of self-evaluation and revision, can take longer
to arrive at that final answer.
So there's that trade-off between accuracy and speed.
Exactly.
And remember, CQOT relies on the AI having access
to relevant knowledge.
So if it hasn't been exposed to the necessary information
or concepts, it won't be able to reason effectively,
even with those critical questions, guiding it.
Like trying to solve a puzzle, but you're missing some pieces.
Precisely.
OK.
And also, I think we touched upon this earlier.
But this research was only tested on a limited set of LLMs.
Right.
We need more research to see how CQOT performs on a wider range
of models and in different contexts.
Makes sense.
So it sounds like there's still a lot to explore,
but the initial findings are really promising.
Absolutely.
CQOT represents this significant step
towards developing AI systems that are not just intelligent,
but also transparent and trustworthy and accessible.
It's paving the way for a future where AI can be a more
reliable and collaborative partner in problem solving.
It's a really exciting time to be following
the developments in AI.
It really is.
We've talked about the potential, the challenges,
and the implications of this research.
But before we wrap things up, I kind of
want to hear more about the specific results
the researchers actually achieved.
OK.
So when we come back, we'll delve into the data
and explore what these findings tell us
about the effectiveness of CQOT in action.
Sounds good.
Stay tuned.
Welcome back to AI Papers podcast daily.
We've been kind of geeking out over this research
on critical questions of thought or CQOT.
It's really cool stuff.
It is, right.
And how it's like enhancing AI reasoning
by giving these LLMs a structured way
to critique their own logic.
Yeah, force them to think a bit more carefully.
Exactly.
So let's get into the nitty gritty of the results.
All right, let's dive in.
What are the researchers actually
fine when they put CQOT to the test?
Well, the results were really compelling.
Remember that MT bench we talked about,
that set of challenging reasoning and math problems?
Yeah.
Well, across almost all the tests,
the LLMs that were equipped with CQOT
consistently outperformed those baseline models,
as well as the ones using other prompting methods.
They actually saw the best performance in 18 out of 20
test cases.
Wow.
So CQOT wasn't just like a slight edge.
It was a clear advantage.
It was a huge advantage.
Yeah.
That's amazing.
And one of the most striking findings
was that these gains weren't just
limited to a single type of LLM.
So we tested it on those open source models like Lama
and Nemotron and the proprietary models like Gemini,
GPC4O, and Claude.
And across the board, they saw these marked
improvements in reasoning accuracy.
So it seems like CQOT could be like a widely applicable
technique, not just like a niche solution.
Yeah.
That's what the data suggests.
That's really cool.
It is.
And remember that surprising finding we talked about earlier,
where the open source Lama model with CQOT
actually outperformed the baseline of the much larger GPT4O.
Yeah.
That was crazy.
It was pretty wild.
It really challenges that whole idea
that bigger is always better.
Right.
Exactly.
When it comes to AI model.
Yeah, it's not always about size.
Wow.
So to really kind of nail down that the critical questions
were the key driver in this improvement.
Yeah.
The researchers did what's called an ablation study.
That's right.
And they removed the critical questions
from the whole CQOT pipeline and ran the tests again.
I did.
And the performance plummeted.
Wow.
So those critical questions really are like the secret sauce.
Yeah, they're the magic ingredient.
They're what's pushing the LLMs to think more deeply
and logically.
Exactly.
So you know this has some pretty exciting implications
for the future of AI development.
OK, like what?
Imagine a world where AI isn't just this black box
spitting out answers.
Right.
But it's like a transparent partner in problem solving.
OK.
Capable of explaining its reasoning in a way
that we can understand and trust.
That's a really powerful vision.
It is.
AI that can not only solve problems, but also teach us
how it arrived at those solutions.
Exactly.
It could revolutionize so many fields
like scientific discovery, medical diagnosis, even
education.
Absolutely.
That's amazing.
It is.
It's a lot to be excited about.
It is.
You know, of course, there's still work to be done.
We need to explore how CQOT performs
on a wider range of LLMs and develop strategies
for mitigating that potential slowdown that
comes with this more deliberative reasoning process.
Yeah.
Those are all important considerations.
They are.
But overall, this research paints a very optimistic picture
of the future of AI reasoning.
I think so.
By giving LLMs the tools to think critically,
we're not just making them smarter.
We're making them more reliable, more transparent,
more collaborative partners.
Couldn't agree more.
Yeah.
This research is really like a testament
to the power of human ingenuity.
To not only build intelligent machines,
but to teach those machines how to think more like us.
Exactly.
To build logic and rigor and a healthy dose of self-reflection.
I love it.
This has been a truly fascinating deep dive
into the world of AI reasoning.
Yeah, it has.
Thanks for joining us on this journey of discovery.
If you want to explore the research in more depth,
be sure to check out the full paper and the code, which
are linked in the show notes.
Definitely check that out.
And don't forget to subscribe to AI Papers Podcast daily
for more exciting explorations into the cutting
edge of AI research.
We'll see you next time.
Until next time.