Welcome back to the AI Papers podcast daily for another deep dive.
And today we're looking at a paper that's asking a pretty timely question.
Yeah.
Can large language models plan paths in the real world like your GPS?
It's a really fascinating question, um, especially, you know, as we see
companies like Volkswagen and Mercedes start to incorporate LLMs into their
car systems.
Yeah.
Uh, you know, we rely on our navigation apps every day, but can these LLMs
really handle the complexities of, you know, actually getting us from point A to
point B?
Right.
It seems like a big leap to go from, you know, writing a poem or something to
navigating a busy city.
Yeah.
Um, so how did these researchers actually test these LLMs?
So, uh, they took three different LLMs, GPT-4, Gemini and Mistrol and tested them
on both drive and routes and something called visual landmark navigation.
Okay.
So think of that, like walking around a city and using landmarks like statues
or buildings to find your way.
Okay.
So they weren't just working with maps on a computer.
They were actually trying to get these LLMs to navigate in the real world.
Exactly.
And they didn't just stick to one type of environment either.
They designed driving routes in urban, suburban and rural settings to see how
the LLMs would handle different road networks and levels of complexity.
That's really interesting.
So it's one thing to navigate like a grid like city, right, but quite another
to deal with winding country roads.
Yeah.
Um, what about the visual landmark navigation?
How did they test that?
So for that, they used a university campus with easy, medium and hard routes.
So the LLM had to figure out how to get from one point to another using only
descriptions of landmarks along the way.
That sounds pretty challenging.
So did these LLMs pass with flying colors?
Can I ditch my Waze app yet?
Well, uh, not quite.
The results were actually pretty surprising.
All three of the LLMs made a lot of errors and some of them are actually pretty serious.
Okay.
Now I'm really curious.
What kind of errors are we talking about?
Like, did they just miss a turn here and there or were things more dramatic?
Well, some of the errors were kind of what you might expect, like missing an exit or
taking a wrong turn.
Yeah.
But they also made some really concerning mistakes, like directing the user to drive
off the road.
Wait, seriously, like telling someone to drive off a cliff?
How is that even possible?
The researchers called these discontinuity errors.
Okay.
It's as if the LLMs were stringing together words and directions without really
understanding the spatial relationships involved.
They would assume a road continued even when it clearly ended on the map.
So it's like they're missing that basic common sense understanding of how the
physical world works.
Like humans, we can look at a map and see that a road ends abruptly.
Yeah.
And we know that's not a place we should be driving.
Yeah.
But the LLMs just seem to ignore that.
Exactly.
And it wasn't just a one time thing.
All three LLMs made these discontinuity errors in both the driving and walking tasks.
Wow. That's kind of alarming.
It makes you wonder what else they got wrong.
Do the researchers give any specific examples of these fails like something we
could really picture?
They do.
There's a great example using a suburban driving route.
Okay.
They compared the LLM generated route to a route generated by Waze, you know, a
popular navigation app.
Right.
Waze took 18 turns over 30 miles.
GPT-4 on the other hand added an extra four miles of discontinuous road to its
route.
So if you were following GPT-4's directions, you'd literally be driving
off road for a good chunk of that trip.
I think I'll stick with my ways for now.
Probably a good idea.
And this highlights one of the key findings of the paper.
LLMs may be great at language, but they don't seem to truly understand the
real world the way we do.
Right.
They're missing that spatial reasoning ability that allows us to navigate
complex environments.
That's a really important point.
It's like they're treating navigation as a word puzzle rather than a spatial
problem.
Yeah.
But what about the time aspect?
Like if I need to be somewhere at a certain time, can these LLMs factor that
into their route planning?
That's another area where they struggled.
Only GPT-4 even attempted to meet time constraints.
The researchers gave it prompts alike, arrive two hours before a game and it
tried to take travel time into account.
So at least it was trying.
Did it manage to get the timing right?
Not always.
And the other two LLMs, Gemini and Mistral, couldn't incorporate time into
their plans at all.
They just focused on getting from point A to point B, regardless of how long it
might take.
So even though GPT-4 showed a little bit more awareness of time, it sounds like
none of the LLMs are quite ready to replace our human design navigation
systems.
Right.
But I'm curious, did any of the LLMs stand out as being better than the others?
Was there a clear winner or did they all pretty much fail equally?
It's interesting because they all failed in the sense that none of them could
consistently plan accurate and safe paths, but there were some subtle differences
in how they messed up.
Okay.
I'm all ears.
What kind of nuances did the researchers pick up on?
Did they like break down the types of errors in any way?
Yeah, they did.
They made a distinction between what they called major errors and minor errors,
which I think is a helpful way to think about it.
Okay.
So major errors sound pretty self-explanatory.
Things that could seriously mislead someone, maybe even put them in a
dangerous situation.
Exactly.
Imagine being told to drive the wrong way down a one-way street or being
directed to an exit that doesn't exist.
Those are the kinds of things that fall into the major error category.
Yes.
I can see how those would be a problem.
What about minor errors?
Are those more like slight inconveniences or things you could easily
recover from?
Exactly.
Think of it like a slight misdirection or an inaccuracy in the
instructions that you could probably figure out pretty easily.
Maybe the LLM tells you to turn left at the second traffic light.
Right.
But it's actually the third one.
Okay.
That makes sense.
So we've already talked about those discontinuity errors, which definitely
sound like they belong in the major category.
Yeah.
What other kinds of major errors do they find?
Well, one that stood out, especially for the driving tasks, was directing the
user to merge onto highways in the wrong direction.
Imagine thinking you're taking the on-ramp to go north.
Oh, wow.
But you accidentally end up going south on a busy highway.
Oh, that would be terrifying.
Merging into oncoming traffic is definitely not something I want
to try.
Right.
And then there were the classic wrong exit or mis-exit errors, which we've probably
all experienced at some point, even with our tried-and-true GPS apps.
It's frustrating enough when your GPS misses an exit and has to reroute you.
But I imagine it would be even more unnerving if you knew it was an LLM
making those mistakes, since there's still such a new technology.
Right.
It really highlights that these LLMs, as sophisticated as they are, still haven't
mastered the art of real-world navigation.
So it wasn't just that they were bad at understanding roads and maps.
They also seemed to struggle with recognizing and describing those real-world
landmarks for the walking tasks.
Exactly.
For the visual landmark navigation, they made major errors, like missing the
destination completely or describing landmarks that didn't even exist.
So it wasn't just a matter of giving bad directions.
They were actually hallucinating landmarks that weren't there.
It seems that way.
And this points to a deeper issue.
LLMs might be missing a fundamental understanding of how things are
spatially related in the physical world.
Right.
It makes you wonder if they're trying to navigate based on word associations
rather than actual spatial awareness.
Like if they've seen the word statue and fountain together in their training data,
maybe they assume those things are always located next to each other in the real world.
That's a really interesting point.
And it kind of gets to the heart of the difference between generating a
language that sounds coherent versus truly understanding the underlying
concepts and relationships behind those words.
It's like they're playing a language game.
Right.
But they haven't quite grasped the rules of the real world game board,
which brings us back to that issue of transparency we talked about earlier.
If these LLMs don't even realize they're making these kinds of errors,
how can we trust them to guide us safely?
It's a valid concern.
And the researchers actually tried to get a handle on this by figuring out how
much driver knowledge would be needed to successfully navigate the routes
generated by the LLMs.
OK.
So they were basically trying to assess whether you would need to be an experienced
driver to avoid getting hopelessly lost.
Exactly.
They categorized the routes as requiring either beginner, intermediate, or expert
driving knowledge based on how severe the errors were and where they occurred.
So if a route was riddled with major errors, you'd probably need to be a pretty
savvy driver to figure out how to get back on track.
Precisely.
And for some of the LLMs, even an expert driver would have had a tough time
making sense of the directions.
It really underscores how much we humans take for granted when it comes to
navigating the world.
We could look at a map, see that a road doesn't connect, and instinctively
know that something's wrong.
Right.
But for these LLMs, that common sense of awareness just isn't there yet.
It definitely highlights the gap between artificial intelligence and human
intelligence, especially when it comes to understanding and interacting with the
physical world.
So we've talked a lot about the types of errors these LLMs made.
Right.
But I'm curious about their overall performance.
Yeah.
Was there one that consistently did better than the others, or did they all pretty
much bomb the test?
Well, like I said earlier, they all failed in the sense that none of them were
reliable enough for real world navigation.
Right.
But there were some subtle differences in their performance.
Some glimmers of potential, you might say.
Ooh, intriguing.
Tell me more about these glimmers of potential.
Did any of the LLMs show particular strengths in certain areas?
For the driving tasks, GPT-4 seemed to have a slight edge, especially in those
suburban and rural settings with longer distances and more complex road networks.
So maybe it was a bit better at handling those trickier routes, perhaps it
gleaned more knowledge about road systems from its vast training data?
That's certainly possible.
But it's crucial to remember that GPT-4 still made plenty of errors, including
those discontinuity errors.
It wasn't perfect by any stretch of the imagination.
Right.
So not quite ready to replace my Google Maps just yet.
What about the other LLMs?
Did Gemini or Mistrol show any strengths?
Interestingly, Gemini actually performed the best overall on the visual landmark tasks.
Really?
That's surprising.
I would have thought that navigating by landmarks would be even more challenging
than following road directions.
What made Gemini better at that?
Well, remember that Gemini was specifically designed with a photo
design with a focus on understanding context and following instructions.
Right.
So perhaps those capabilities gave it a bit of an advantage when it came to
navigating using landmark descriptions.
That makes sense.
Being able to accurately interpret those landmark descriptions is essential for that task.
Yeah.
But even though Gemini did better with landmarks, it still wasn't perfect, right?
Exactly.
It still made significant errors, especially on those more challenging routes where
the landmarks were more spread out or the descriptions were more ambiguous.
So the bottom line is that none of these LLMs were consistently reliable enough to
trust with real world navigation.
That's the key takeaway from this research.
And while it might seem like a bit of a setback for the LLM hype train, it's actually
a really valuable finding.
By identifying these limitations, it sets the stage for future research to focus on
improving LLM capabilities in this crucial area.
It's like any new technology.
There are going to be growing pains and unexpected challenges.
Right.
But by understanding where these LLMs are falling short, we can start to develop
solutions and make them more robust and reliable.
Exactly.
It's all about progress, not perfection.
And that's what makes this kind of research so important.
It's not just about showcasing what LLMs can do, but also about honestly assessing
their limitations so that we can move forward in a responsible and informed way.
We've covered a lot of ground here, and I'm sure our listeners are eager to hear
what the researchers propose for the future of LLMs and navigation.
What are some of the key takeaways and recommendations they offer?
They actually had some really thought-provoking ideas about how to make LLMs
better navigators, but we'll dive into those right after a quick message.
Stay tuned.
Welcome back to the AI Papers podcast daily.
We've been talking about these large language models and how they're not quite
ready to be our personal chauffeurs just yet.
But you know, the paper we're discussing didn't just point out the problems.
They also offered some really interesting ideas about how to improve LLMs for navigation.
Yeah, that's right.
It's not enough to just say, hey, these LLMs aren't very good at getting around.
You know, the researchers really dug into what's causing these errors and came up
with some concrete suggestions for how to make LLMs better navigators.
Okay, I'm ready for some solutions.
What's the first big idea they propose?
One that really stood out to me was the idea of building in what they call reality checks.
Okay.
Right now, LLMs tend to operate in this kind of closed loop.
They take in text data, they process it and they generate more text, but they don't
always cross-reference their output with the real world.
So if they tell you to turn left onto a road that doesn't exist, there's no part of
the LLM that's like, wait a minute, that can't be right.
Exactly.
They're missing that layer of common sense that humans have where we can look at a map
or think about our surroundings and say, this doesn't make sense.
So the researchers suggest giving LLMs the ability to check their output against
external data sources.
So like having the LLM double check its directions against a real time map or maybe
even traffic data, that could definitely help prevent those discontinuity errors where
they send you driving off a cliff.
Exactly.
And it goes beyond just maps.
They could also incorporate things like weather reports, business hours, or even
reviews to see if a particular route is known for being dangerous or difficult to navigate.
It's about giving the LLM a way to ground its output in the real world and make sure
it's actually feasible.
That makes a lot of sense.
It's like giving them a dose of that human skepticism that we often take for granted.
I like that analogy.
And speaking of skepticism, the second big idea they propose is all about increasing
transparency.
Okay.
Right now LLMs can be a bit overconfident even when they're tackling tasks they haven't
quite mastered yet.
It's like they haven't learned that sometimes it's okay to say, I don't know.
Right.
So the researchers are saying that future LLMs need to be more upfront about their limitations.
Instead of just spitting out directions, they should be able to say something like, Hey,
I'm not very familiar with this area.
So you might want to double check these directions.
I love that.
It's about empowering the user to make informed decisions about how much they trust the LLM.
It's that honesty and transparency that will ultimately make these systems more reliable.
Absolutely.
And the final idea they put forward is something we've touched on already, the potential of
smaller, more specialized models.
Maybe trying to create these giant, all-knowing LLMs isn't the best approach, especially when
it comes to tasks that require very specific skills like navigation.
It's like the old saying, jack of all trades, master of none.
Yeah.
Maybe we need to focus on creating LLMs that are experts in specific domains like navigation.
Exactly.
So the researchers suggest exploring the development of smaller LLMs that are trained specifically
for navigation.
Yeah.
Using data sets and algorithms that are tailored to spatial reasoning and route planning.
That makes a lot of sense.
If you're building a house, you want a general contractor.
Yeah.
But if you need brain surgery, you want a neurosurgeon.
Yeah.
It's about finding the right tool for the job.
I couldn't set it better myself.
So while this research might seem like a bit of a reality check for LLMs, it's actually
incredibly valuable.
Right.
It's highlighting the areas where we need to focus our efforts to make these systems
truly useful and reliable for real-world navigation.
Yeah, it's been a fun one.
It's been a fascinating deep dive.
Yeah.
It's clear that LLMs have a lot of potential, but they're not quite ready to replace our
trusty GPS apps just yet.
Right.
But by highlighting these challenges and proposing solutions, this research is paving the way
for a future where LLMs can truly enhance our ability to navigate the world.
I completely agree.
And I think it's a reminder that AI development is an ongoing process.
It's not about creating perfect systems overnight, but about constantly iterating, learning from
our mistakes and striving to make these technologies more beneficial and reliable for everyone.
And that's what makes this field so exciting.
There's always something new to discover and new challenges to overcome.
So keep those questions coming and stay tuned for our next deep dive into the world of AI research.
Until then, keep exploring, stay curious, and maybe double check those LLM generated
directions before you hit the road.