Welcome to the AI Papers podcast daily.
Today we're diving deep into this paper
that's making waves, thinking LLMs,
general instruction, falling with thought generation.
It's tackling a really intriguing question.
Can we teach AI to think like we do?
So this paper focuses on large language models,
or LLMs, which are essentially powerful AIs
trained on massive amounts of text data.
What's fascinating is that these LLMs
usually have a fixed budget for processing information,
regardless of the task's complexity.
So whether it's a simple question like,
what's the capital of France?
Exactly.
Or a complex one requiring in-depth analysis.
Yep.
The LLM uses the same amount of computational power.
That doesn't seem very efficient, does it?
Exactly.
Think about it.
When you face a challenging problem,
you don't just blurt out the first answer that comes to mind.
Right.
You take time to think, maybe jot down some notes,
way different options before arriving at a well-reasoned solution.
Traditional LLMs lack that crucial internal thinking stage.
And that's where this paper comes in.
It introduces the concept of thinking LLMs,
models that are trained to generate internal thoughts
in plain language before giving a response.
It's like the AI is having a silent conversation with itself,
brainstorming and refining his ideas before presenting
the final output.
Precisely.
And this thinking happens behind the scenes,
so the user only sees the polished final answer.
Gotcha.
But researchers can peek into this process
to gain insights into how the AI is reasoning and problem
solving.
That's really cool.
But how do you actually teach an AI to think in the first place?
It's not like we can just tell it to start thinking
and expect it to magically work right.
Right.
This is where the paper gets really interesting.
OK.
They've developed a novel training method
called Thought Preference Optimization, or TPO.
TPO.
Essentially, they train the LLM to generate multiple thought
response pairs for a given instruction.
So it's like the AI is coming up with different approaches
to solve a problem.
Exactly.
Then a separate judge model comes into play.
OK.
This judge is trained to evaluate
the quality of the responses.
But here's the key.
It completely ignores the internal thoughts.
Interesting.
It only focuses on the final output.
So the AI's thought process is a black box.
And the judge only cares about the end result.
That's a clever approach.
But how does that feedback loop actually teach the AI
to think better?
The magic lies in a technique called preference optimization.
Based on the judge's feedback, the LLM
learns to adjust its internal thinking process
to produce better and better responses.
So like.
It's like a chef trying different recipes in the kitchen.
OK.
But only the food critics get to taste the final dish.
That's a great analogy.
The chef doesn't reveal their process,
but they learn what works based on the critics' feedback.
Yeah.
So in this case, the LLM is the chef.
The internal thoughts are the recipes.
And the judge is like the food critic.
Right.
That's it.
And what's remarkable about TPO is that it doesn't need
any labeled thought data.
Right.
Which is incredibly difficult and expensive to collect.
The AI is free to explore different thought patterns
and figure out what leads to the best results on its own.
This is fascinating stuff.
But did they actually put this TPO method to the test?
Did they see any real improvements in how the AI performs?
They did.
They ran experiments using a Llama 3.8B Instruct model.
OK.
And tested it on benchmarks like Alpacheval and Arena Hard,
which measure how well an AI can follow general instructions.
So they basically gave the thinking LLM a series of tasks.
Yeah.
And compared its performance to traditional LLMs.
Yes.
And the results were quite impressive.
At first, the thinking LLMs actually performed worse
than the standard LLMs.
Really?
Which is to be expected.
It's like trying to solve a complex math problem in your head
when you're used to writing down all the steps.
Yeah.
It takes time to adjust to that new way of thinking.
But after several rounds of training with TPO,
these thinking LLMs started outperforming the baseline models.
So it's like with any new skill practice makes perfect.
Right.
And the AI is learning to think in a way that actually
boosts its effectiveness.
Yeah.
It's amazing.
What kind of improvements did they see?
The TPO trained model achieved a win rate of 52.5%
on Alpacaival, putting it in third place on the leaderboard,
just behind much larger models like GPT-4.
Really?
And on Arena Hard, it hit a 37.3% win rate,
exceeding expectations.
Wow.
Those are some compelling results,
especially considering it was up against much larger
and more resource-intensive models.
It seems like this TPO method could really be a game changer
in the field.
But earlier, you mentioned something
about the AI using natural language
for its internal thoughts.
Can you elaborate on that?
Why is that so important?
Wouldn't it be more efficient for the AI
to think in some kind of code?
That's a great question.
Using natural language for a thought generation
offers several advantages.
Right.
Remember, these LLMs are trained on vast amounts
of human written text, like books, articles, and code.
By thinking in natural language,
they can tap into the pattern structures and knowledge
embedded in that data.
So it's like the AI is using the same language we use
to communicate and understand the world, which
gives it a richer foundation for its thinking process.
Exactly.
It's not just manipulating symbols in a vacuum.
It's drawing on the same linguistic tools
we use to reason, plan, and create.
This also makes the AI's thinking process
more transparent and interpretable.
Researchers can actually see how the AI connects ideas,
explores different options, and arrives at its conclusions.
That's crucial for building trust in AI systems.
Right.
If we can understand how an AI is making decisions,
we're more likely to accept and rely on its output.
But you mentioned earlier that these internal thoughts
are hidden from the user.
Right.
So while researchers might benefit
from this interpretability, wouldn't it
be helpful for users to see the AI's thought process as well?
That's a great point.
There's a lot of debate about the level of transparency
that's appropriate for different AI applications.
Yeah.
In some cases, like medical diagnoses,
it might be crucial for users to see the AI's reasoning steps
to ensure fairness and accountability.
So in those situations, it's not just
about getting the right answer.
It's about understanding how the AI got there.
Exactly.
But for tasks like writing a poem,
the user might prefer a more streamlined experience focusing
on the final output without being bogged down
by the AI's internal deliberations.
It seems like it's about striking a balance between transparency
and user experience.
And the right approach might vary depending on the task.
Now, one thing that really sit out to me in the paper
was the finding that thinking LLMs initially performed
worse than standard LLMs.
Right.
Especially on tasks involving logic and reasoning,
like math problems.
Why is that?
It's interesting because even though TPO showed promise
in general instruction following,
it did struggle with math.
The researchers believe that their experimental setup wasn't
optimized for math-heavy tasks.
The models were mainly trained on diverse instructions
with only a small portion focused on math.
So it's like trying to teach someone to play the piano
by mainly giving them guitar lessons.
They might grasp basic musical concepts,
but their piano skills won't be great.
Precisely.
The researchers suggest that including more math-specific
instructions during training could
potentially bridge this gap.
That makes sense.
But the fact that this TPO method works
across such a diverse range of tasks,
even with that limitation, is pretty impressive.
It challenges our traditional ideas of what AI is capable of.
Are there any other limitations or challenges
that the researchers highlighted in the paper?
Yes.
They did point out a few areas for improvement.
One key challenge is controlling the length of the AI's thoughts.
If they get too long, it can become computationally expensive
and slow down the response time.
Right.
You don't want the AI getting stuck
in an endless internal monologue.
Exactly.
They acknowledge the need for better ways
to manage thought length.
Ensuring the thinking process is comprehensive,
but also efficient.
Another area for exploration is the use
of different thought prompts.
The prompts used to initiate the AI's thinking
and influence the types of thoughts it generates.
It's like providing the AI with different starting points
for its brainstorming session.
A prompt, like think step by step,
might lead to a different thought process compared to a prompt,
like consider multiple perspectives.
Precisely.
And lastly, remember, this research
was conducted on relatively small language models.
How TPO will perform on the massive LLMs being developed
today is an open question.
OK.
It seems like there's still so much to explore in this area,
in the world of thinking LLMs, that even with these limitations,
the research we've discussed today is a significant step
forward.
It really challenges our understanding of AI's potential.
I completely agree.
This ability to teach AI to think internally, to plan draft,
and evaluate its own work could be transformative for the field.
Absolutely.
So as we wrap up this episode of the AI Papers Podcast
daily, I'll leave our listeners with a question to Pond.
What do you find most intriguing about the idea of thinking LLMs?
And what potential applications or future developments
in this area excite you the most?
Thanks for joining us on this deep dive
into the world of thinking LLMs.
We hope you found it insightful.
Until next time, keep exploring the fascinating world of AI.