So we pretty much accepted this tradeoff, hadn't
we, in the world of generative AI? Yeah, the
idea that safety training, you know, making models
helpful and harmless. Right. We figured it kind
of clipped our creative wings for good. Right.
Less chaos, sure, but maybe less of that real
spark, too. Well, it turns out that whole story
might be, well... Not quite right. There's this
new Stanford study suggesting the creative potential
wasn't really damaged. Not damaged, just hidden.
Exactly. Trapped, maybe is a better word. Waiting
for the right key. Welcome back to the Deem Dive.
If you've ever felt that AI, like chat GPT, is
getting kind of predictable, that boring AI problem
where you ask for, say, five unique ideas and
you just get five slightly different takes on
the same safe thing. Yeah. You're in the right
place today. Definitely. Our mission today is
to really dig into this technique called verbalized
sampling. We're going to look at why AI got boring
in the first place. And spoiler, it might be
more about us than the AI. Then we'll introduce
this like super simple eight -word phrase that
seems to unlock things. We'll look at some really
cool examples and then, yeah, the practical steps
you can take right now. Okay, so let's start
with the problem itself, this idea of mode collapse.
That's the term, right? Yeah, mode collapse.
It's when the AI just keeps giving you the same
kind of answer, the safe middle -of -the -road
stuff. Yeah, we all sort of pointed the finger
at the alignment training. Yeah. You know, RLHF,
DPO. The safety methods. We did. The assumption
was, okay, these guardrails are necessary, but
they kind of squashed the imagination in the
process. But the Stanford folks looked somewhere
else. They looked at the humans, specifically
the human raters who provide the feedback that
trains these models, the ones grading the AI's
answers. And what about them? What did they find?
They found us. Basically, human psychology. When
we rate AI responses, we bring all our own subconscious
biases to the table. The AI just got really good
at figuring out what we tend to rate highly.
Hang on. So if I rate an AI answer well, just
because it's like easy to understand or familiar,
I'm actually training it to be less creative.
In essence, yeah. That's what the research suggests.
There are these like four key psychological biases
they identified. First, the mere exposure effect.
Mere exposure. Yeah. We just tend to prefer things
because we've seen them before. They feel comfortable.
So a truly novel or weirdly creative answer,
it often gets a lower score than something familiar.
Okay. So we value comfort over genuine novelty.
That feels very human. It is. Second, there's
the availability heuristic. If an idea pops into
our head easily, we think it's good. We mistake
mental ease for quality. Which means really brilliant,
maybe complex ideas get penalized because they
take more effort to process. Exactly. The AI
isn't optimizing for brilliance necessarily.
It's optimizing for what we rate as good, which
often means easy and familiar. Wow. Okay. What
else? Third, processing fluency. Similar idea.
Answers that are smooth, simple, easy to read.
They just feel better, higher quality than something
that might be more complex or challenging. And
the last one. Schema congruity. We like answers
that fit what we already believe. Things that
confirm our existing mental models or schemas.
Stuff that challenges us. Not so much. Gets lower
ratings. So the AI didn't lose its creativity.
It just learned to be agreeable. To fit in with
our predictable preferences. That's pretty much.
It learned to give us what we seemed to want.
Based on those ratings, the wilder potential,
the stuff from its original massive training
data, it's still in there, just kind of buried
under this layer of please the user. OK, but
then if it's been trained so hard on our biases
for so long, how can just the simple prompt fix
that? How does that overcome all that alignment?
Because the prompt changes the game. It asks
the AI to be a scientist, not just a good student.
All right, let's get to the solution then. Verbalize
sampling. And the key you said is just eight
words. Eight crucial words. With their probabilities.
That's the magic phrase. Okay. So how did that
fit into a prompt? What's the structure? Right.
So your old prompt might be something simple
like generate five ideas about topic. Predictable
results, right? Yep. The usual suspects. The
new structure is generate five responses about
topic with their probabilities. See the difference.
You're explicitly asking for the probability
score for each response. Okay. With their probabilities.
Yeah. Why does that change things so fundamentally
inside the AI? Because asking for the best answers
triggers that learned behavior give the safest,
most common, highest rated stuff. But asking
for answers with their probabilities... that
tells the ai don't just give me the popular stuff
scan your entire knowledge base pull a random
sample even from the weird corners and then just
report the probability of each one ah so it's
not filtering based on pleasing me anymore it's
just reporting what's possible exactly it's like
asking a baker for their top three cakes versus
asking them to list all the cakes they can make
and maybe how often each gets ordered you suddenly
discover they can make i don't know lavender
honey cake even if it's rarely requested that
makes sense and you You mentioned proof. Examples.
Yeah, the examples are quite striking. Take story
writing. Ask for a story about a bear. The old
way. You get five slightly different versions
of like, Barry the bear walks by the river. The
boring bear. Oh, I know Barry the bear. I've
gotten that story or versions of it so many times.
Just walking. maybe sniffing some berries right
use verbalized sampling ask for stories with
their probabilities and suddenly you get genuinely
different concepts the lost cub searching for
its mother the clever bear who outsmarts the
bees for honey the ancient guardian bear of the
mountain pass different plots different tones
okay that's a definite improvement does it work
for other things like genre absolutely they tested
it with a starting sentence he was still in the
building The standard prompt always defaulted
to a basic crime story. Detective Miller, flashlight
beam cutting through the dark, you know the drill.
Yep. Standard procedure. With verbalized sampling.
Boom! Totally different genres popped out. A
suspense horror story about some ancient presence
in a library. A sci -fi piece about an engineer
trying to contain some kind of energy distortion.
Even like a metaphorical story about being trapped
in the labyrinth of one's own memories. Wow.
Yeah, that's the kind of diversity we're talking
about. Did they try it with image prompts too?
They did. Same principle. Old prompt. Astronaut
riding a horse. You get five photos, basically
slightly different angles, new prompt with probabilities,
five distinct autistic concepts. Sci -fi movie
poster style, retro neon vapor wave, a children's
book illustration in watercolor, and get this,
even a broke oil painting portrait of the astronaut
on the horse. A broke oil painting? Seriously.
It unlocks the conceptual range, not just minor
variations. Okay, that's impressive. But you
mentioned this taps into the wild pre -training
data. Does that mean it works the same on, say,
a small model versus a giant one like GPT -4?
Ugh, good question. No, bigger models actually
show much larger diversity gains. It's a skill
that scales up. Yeah, let's dig into that scaling.
The lab data showed that the bigger models think
GPT -4 class, the latest Gemini models, they
got diversity improvements that were 1 .5 to
2 times greater than what smaller models showed.
Whoa, okay, so 1 .5 to 2 times more diverse just
by using this prompt on a bigger model. Exactly.
Imagine what that means for future, even more
powerful models. This technique just gets better
as the AI gets smarter. That really shifts how
we should think about prompting going forward.
It's a future -proof skill almost. Kind of is.
And it's not just on or off either. You can actually
tune the creativity level. How do you do that?
By tweaking the prompt. You can add a condition,
like generate five responses about topic with
their probabilities below 0 .10. You're telling
it, look deeper, give me the rarer stuff, the
long tail ideas. It's like a creativity dial
you can adjust. The creativity dial. I like that.
And you mentioned that the diversity isn't just
for fun, right? It's actually functional. Something
about synthetic data. Yes, this is super important.
They used verbalized sampling to generate a diverse
set of, say, math problems. Then they used that
diverse data to train a different, smaller AI
model. Okay. And what happened? The model trained
on the diverse data got significantly better
on math benchmarks. But here's the kicker. They
also trained a model on the boring, predictable
data from the standard prompt. That model actually
got worse. Worse. Worse. It proves that diversity
in training data isn't just nice to have. It
makes the resulting AI functionally better, more
robust, boring in, boring out, and maybe even
dumber out. Okay, that's a huge point. But the
big question then is safety. If we're bypassing
the standard change responses, are we compromising
safety, accuracy? That's the crucial test, right?
And the results were really encouraging. For
factual accuracy, they used common sense reasoning
tests. Verbalized sampling performed just as
well as the standard prompts. Sometimes the creative
variations even scored the highest. No hit to
truthfulness. Okay, that's good. And safety.
Harmful prompts. The safety guardrails held strong.
On the Strong Reject benchmark, which tests refusal
of harmful requests, the models using verbalized
sampling still refused over 97 % of the time.
Same level as standard. So it doesn't break the
safety rules. Right. And interestingly, how it
refused was more diverse. Instead of the same
robot, I cannot fulfill this request message,
it gave different, sometimes more helpful explanations
for why it was refusing. So, creativity unlocked,
but not chaos. Okay, this all sounds incredibly
promising, but for the average person using this,
what's the catch? What's the real -world cost?
Well, the main cost is resources. Generating
five diverse answers takes about five times the
compute, five times the time, five times the
potential cost, mid -role sponsor replaceholder.
Right, so let's talk about those trade -offs,
the fine print. As you said, it costs more time,
compute, potentially money, depending on the
API. Yep, and it works best on the really big
models, so if you're using smaller ones, the
effect might be less dramatic. And it's definitely
not for everything. Asking what's the capital
of France with probabilities is overkill. Yeah.
Probably just get Paris with a probability near
1 .0. Exactly. It's for creative tasks, brainstorming,
exploration, not simple fact retrieval. And there's
a user cost too, right? Getting five diverse
options is great, but you have to do the work
now. Meaning you have to read all five. evaluate
them, decide which one is actually best for your
needs, maybe combine elements. It requires more
thinking on your part than just getting one safe
answer. Yeah, that's fair. I mean, I still wrestle
with prompt drift myself sometimes, tweeting
things constantly. So knowing I have to actively
sift through more options. It's a trade -off,
but... The potential reward seems worth it. It's
work, but good work. It is. But the good news
is you can try this right now pretty easily.
Method one is just direct prompting in your usual
chat interface. How does that work? Just type
the phrase. Kind of, but it helps to give it
more structure. The researchers recommend using
simple tags, almost like XML, to make it really
clear what you're asking for. Okay, like how?
Can you give an example? Sure. You'd start with
an instruction block, something like... Instructions
generate five responses. Put each in a response
tag. Each needs text and a numeric probability.
Sample randomly from the whole distribution.
Instructions. Then after that block, you put
your actual question, like... Write a story about
a lonely robot. Ah, so the tags, instructions,
response, text, probability help the AI understand
it's not a normal chat. It's supposed to format
the output like a structured report. Exactly.
It forces it out of its conversational habits
and into this more analytical sampling mode.
It helps bypass that polite and boring layer
we talked about. Got it. That makes sense. And
for people who want this all the time. That's
method two, system prompt integration. If your
AI tool has custom instructions or system prompt
settings, you can put the core instructions in
there. Tell it to always sample from the full
distribution, maybe even favor the lower probability
tails. Make creativity the default. Right, set
it and forget it, kind of. So, applications.
Obviously brainstorming. Huge for brainstorming.
Getting genuinely different starting points.
Content creation. Finding new angles or structures
for articles, scripts, whatever. And image generation.
Imagine feeding five really unique concepts into
mid -journey or dally instead of five minor variations
of one idea. Yeah, that opens up a lot. Yeah.
Okay, stepping back, thinking about everything
these models know, that whole vast distribution
of knowledge, what's the biggest single takeaway
from this verbalized sampling breakthrough? I
think it tells us we don't have to accept a tradeoff
between safety and creativity. That was a false
choice. Human bias was the limit, not the AI
itself. Hashtag, hashtag, outro. That really
is a profound shift in perspective. So let's
quickly recap the two big ideas here. First,
the AI creativity we thought alignment training
had dampened. It wasn't gone, just hidden, masked
by our own human preference for the easy, the
familiar, the safe. Our biases trained it to
be boring. And second, that simple eight -word
phrase with their probabilities acts like a key.
It changes the AI's task from give the best answer
to report a sample of possible answers, letting
it access that deeper, wilder knowledge it was
trained on. It makes you wonder, doesn't it,
if this creative ceiling was just a mirage caused
by how we ask the questions. What other incredible
abilities might be lying dormant in these models?
Exactly. What else are we not seeing? Because
we're not asking in the right way. The limit
might truly be our own imagination in figuring
out how to unlock it. A powerful thought to end
on. We definitely encourage you listening to
try method one, the direct prompting with those
tags. See what you discover. Thanks for joining
us for this deep dive. We'll see you next time.