All right, so today we're diving into this paper.
Okay.
And it's called LLM hallucination reasoning with zero shot knowledge test.
And she.
Yeah.
So it's kind of a mouthful, but it's looking at how we can tell when these large
language models or LLMs are making stuff up.
You know, that's one of the biggest challenges in AI right now.
Yeah.
Like AI fake news.
Yeah.
And it's really interesting because this paper goes a step further.
Okay. It doesn't just try to detect when an LLM is hallucinating.
It tries to understand why it's happening.
Okay.
So it's getting into the AI.
It's crucial for figuring out how to make AI more reliable.
Brain a little bit to see what's going on.
Can you give us a quick rundown of how these LLMs actually work?
Because I think some of our listeners might be new to AI.
Basically, LLMs are trained on these massive amounts of text data.
And they learn to predict the next word in a sequence based on the words that came before it.
So it's like a really fancy autocomplete.
Yeah.
Like a super powered autocomplete on your phone, but on a much
rendered scale.
So they're good at predicting what word comes next.
But sometimes they get it wrong and that's when the hallucinations happen.
Exactly.
And the researchers in this paper, they actually break down hallucinations
into three different types.
Okay.
Aligned, misaligned and fabricated.
Okay.
So give me.
Aligned means the AI knows the correct answer and gives it to you.
Aligned.
Misaligned means the AI knows the answer.
But for some reason it gives you a slightly wrong or contradictory one.
Okay.
And fabricated means the AI has absolutely no clue what the answer is.
And just makes something up that sounds plausible.
So that's like when I ask someone a question and they just start rambling,
hoping that I won't notice that they have no idea what they're talking about.
A great analogy.
And the problem is that previous methods for detecting hallucinations
couldn't really distinguish between these three types.
Okay.
They could tell you something was off, but not why.
So this research is trying to get to the bottom of why the AI is hallucinating.
Exactly.
And tell me about this new method they came up with.
They call it MKT, which stands for Model Knowledge Test.
Okay.
It's basically a knowledge test for AI.
Like a pop quiz.
It's a bit more subtle than that.
The researchers use this technique where they kind of play around with the words
and the questions they ask the AI.
Let me give you an example from the paper.
Imagine you ask the AI, what is the habitat of a pika?
Okay.
I'm picturing this cute little pika.
Well, MKT would then change the word pika slightly,
maybe replacing it with a made up word like snuck utzow.
Snuck utzow.
What is that?
So if the AI's answer changes significantly when you tweak pika,
it suggests that it actually has some knowledge about real pikas
and is using that knowledge to shape its response.
So it knows that pika is a real thing.
But if the AI keeps giving similar sounding answers, even with nonsense words,
it's a red flag that it's probably fabricating information.
So they're trying to trick the AI?
Kinda.
Yeah.
That's pretty smart.
It is.
And the results were really promising.
Yeah.
MKT was particularly good at detecting the fabricated type of hallucination.
Okay.
Much better than previous methods.
So this could be a big step for making AI more reliable.
It has the potential to be.
Yeah.
Now, are there any limitations to this research?
Well, like any research, it has its limitations.
One thing the researchers pointed out is that the method they used to distinguish
between aligned and misaligned text after they've used MKT can be a bit time consuming.
So there's still some room for improvement there.
There is.
In terms of efficiency.
Yeah.
But the potential applications of this research are pretty exciting.
Yeah.
Think about how this could be used to make sure AI gives us accurate information.
Yeah.
In areas like healthcare education or even journalism.
That's a good point.
Yeah.
Because we need to know that the AI is giving us correct information,
especially in those fields.
But it's also important to remember that AI is still a tool.
Absolutely.
And it's up to us to use it responsibly.
We need to be aware of its limitations and always be double checking its output.
This deep dive has already given me a lot to think about.
Yeah.
We've learned how AI can hallucinate, how this new method can help us detect
and understand those hallucinations and what this all means for the future of AI.
Yeah.
And where do we go from here?
What are your thoughts?
Yeah, I think it's a good first step.
You know, like we're moving in the right direction,
figuring out why it's doing these things.
If we can figure out why AI hallucinates,
we can start to develop strategies to minimize those errors
and make it a more trustworthy tool.
We've covered the basics of how LLMs work
and how this new MKT method is trying to address the problem of hallucinations.
What else did the researchers find?
Well, they put their method to the test.
Okay.
Using two new data sets they created specifically for this research.
The NEC data set and the Biography data set.
Right.
Let's dive into those now.
It sounds good.
I'm curious to hear about that.
So you're probably wondering how the researchers actually tested this MKT method.
Yeah.
Well, they created these two data sets, the NEC data set and the Biography data set
to see how well MKT could detect different types of AI hallucinations.
So what are these data sets?
Well, think of them as like specialized testing grounds for the AI.
Okay.
The NEC data set is like a giant trivia challenge
covering all sorts of topics from animals and sports to medicine.
They included questions about both real and made-up concepts.
So they're really trying to put it through his paces.
Exactly.
And for the questions about real concepts,
they made sure the answers could be verified using Wikipedia.
Okay.
That way they could be confident that the AI was genuinely knowledgeable
if it answered correctly.
Makes sense.
Right.
And for the made-up concepts, well, they knew the AI would have to fabricate answers for those.
Yeah.
They also used some clever tricks to get a mix of aligned and misaligned responses
for the real questions.
Okay.
Sometimes even prompting the AI to introduce factual errors.
So they were really trying to trip it up.
Yes.
They wanted to make sure the data set was challenging enough to really test the limits of MKT.
Okay.
Now, the Biography data set focused on biographical information.
Okay.
They started with a list of real people from Wikipedia
and then created fictional variations of those names,
sometimes combining them with incorrect job titles.
So they're like making fake profiles based on real people.
Exactly.
And the goal was to see if the AI could differentiate between the real biographies
and the made-up ones.
Okay.
They also developed this technique to identify which names the AI had more knowledge about.
Mm-hmm.
They generate a biography, remove the name, and then ask the AI to guess the missing name.
It's like a game of who am I?
That's a great way to put it.
But for AI?
And if the AI could consistently guess the right name,
it suggested it actually had enough information about that person
to create a believable biography.
So they put a lot of effort into making sure that these data sets...
They did.
...were really good.
And the results of this research were quite impressive.
MKT performed remarkably well on these data sets,
showing its potential to be a valuable tool for understanding and addressing AI hallucinations.
This is all so fascinating.
We've learned so much about the nuances of AI hallucinations
and how researchers are developing these innovative techniques to detect and understand them.
It's really cool stuff.
But what does the success of MKT tell us about the future of AI?
I think it points to a future where AI systems are not only more intelligent,
but also more transparent and accountable.
Can you unpack that a little bit?
Sure.
As AI becomes more deeply integrated into our lives,
it's crucial that we have ways to verify the accuracy and reliability of its outputs.
MKT is a promising step in that direction.
It provides a way to look inside the black box of AI
and understand how it's arriving at its conclusions.
Right.
It's not enough to just accept what it's saying at face value.
Right.
We need to be able to ask,
how do you know that?
What are your sources?
And as techniques like MKT become more sophisticated...
Exactly.
...and widely adopted.
I think they will lead to the development of AI systems that are more transparent,
okay.
...explainable and trustworthy.
So we're kind of moving from this world of just blind trust in AI to...
I like that analogy.
...a little more collaboration.
It's not about replacing human intelligence with artificial intelligence,
but rather augmenting it.
And ensuring that we have the tools to evaluate what it's saying.
Absolutely.
This is really highlighting...
It's a really interesting deep dive so far.
How important it is to think critically about AI.
Yeah, we can't afford to be passive consumers of AI-generated content.
We need to engage with it, actively question it, and hold it accountable.
I couldn't agree more.
All right, before we move on, I have a question for you.
Okay.
Knowing that AI has this potential to hallucinate,
how will you approach...
That's a great question.
...using it in the future?
Well, I think I'll approach AI with a healthy dose of skepticism,
but also a sense of wonder.
I'll be curious to see what it can teach me,
but also be mindful of its limitations.
What about you?
Yeah, I think a similar approach.
Okay.
Kind of that balance of curiosity, but also caution.
Yeah.
I want to see what it can do, but I'm also going to be checking everything,
making sure that I'm not just blindly accepting what it's telling me.
I completely agree.
All right, well, let's move on to the final part of our deep dive...
Okay.
...where we'll explore some broader implications and wrap up our discussion.
So we've talked about how AI can hallucinate
and how researchers are using techniques like MKT
to detect and understand these hallucinations.
But what does this research tell us about the future of AI?
Well, one thing that struck me about this paper
is how it shifts our thinking about AI hallucinations.
How so?
Well, in the past, we've kind of seen these hallucinations as like a bug,
a glitch in the system.
A hiccup in the AI's thinking.
Exactly, something that needs to be fixed.
But this research suggests that hallucinations might be more
than just these random errors.
They could actually offer valuable clues
about how AI models process information and build knowledge.
So instead of just being a nuisance,
they might actually help us understand how AI works.
Exactly.
That's cool.
By studying the patterns in these hallucinations,
we might gain insights into how AI models learn reason
and generate language.
So it's like we can look at the hallucinations to kind of see...
It's a really interesting way to think about it.
How it's thinking.
And it could even help us develop new techniques
to make AI models more accurate and reliable.
So we're not just trying to eliminate the hallucinations
we're trying to learn from them.
Exactly.
It's all about understanding the root causes of these errors
so we can develop better solutions.
This has been an incredible deep dive.
It has.
You know, we've explored this strange world of AI hallucinations,
learned about this innovative MKT method,
and considered the broader implications for the future of AI.
Any final thoughts?
I think this research really highlights the importance
of being thoughtful and critical when interacting with AI.
We need to be aware of its limitations
and not blindly accept everything it tells us.
We can't just treat it like a magic oracle.
Right. It's a powerful tool, but it's still a tool.
And like any tool, it needs to be used responsibly
and with a critical eye.
Well said.
Yeah.
This has been a fantastic deep dive.
It has.
We've learned so much about how AI works,
where it can go wrong,
and how we can make it more reliable in the future.
Thanks for joining me.
It's been my pleasure.
And to our listeners, thanks for tuning in to The Deep Dive.
We'll see you next time for another fascinating exploration
of the world of AI.