Continuing our investigation of benchmarks, and this will become more real when we actually see some
action.
So bear with me while we do this, but it is very important and useful background information.
We're going to look at three specific benchmarks used to test, uh, more, more, uh, specialized
skills.
And the first of them is is used for evaluating chat between models, um, particularly in the in face
offs between models in things like arenas, as we will see.
And the benchmark is an Elo rating.
Uh, if you are a chess player or familiar with with chess Elo ratings, Elo is a rating that you can
give to competitors in a sport or some sort of a game where there is a loser for every winner, where
it's a zero sum game, um, and uh, uh, based on the outcomes of these kinds of, of games, uh,
you can give people this relative measure that affects their strength compared to others based on their
performance in head to head face offs.
So you'll see examples of of those from an arena later and that will that will bring it to life.
Um, but it's a, it's used in particular the ones that I'm going to show you are used to evaluate the
chat abilities, the instruct abilities of these models.
And then the next two are about coding.
Human eval is a very well known Python coding test.
It's 164 problems.
Uh, it's about writing code actually based on Python doc strings.
Uh, and it's something where models have become increasingly effective at and then, uh, multiple
or maybe it's pronounced multiple.
Uh, I expect just multiple is the same thing, but translated to 18 different programming languages.
So this is more of a wider variety of programming skills across different areas.
Let me take a moment now to mention Mentioned the limitations of these benchmarks, which is such an
important point, and it's something that that cannot be stressed enough.
Benchmarks are useful for us for comparing, of course, the different where different models shine
and where they're not not intended to be used.
But there are problems with benchmarks, and you need to be aware of them when you look at them.
One of them is that they are not consistently applied.
So, um, depending on where you're seeing the benchmark, particularly if it's something like a press
release from a company, they it's really up to them how they measured the benchmark, what kind of
hardware they used and stuff like that.
It's not like there's a gold standards that, that, um, put these measurements on rails.
So everything has to be taken with a pinch of salt.
If it's been published by a third party, there can be too narrow in scope, particularly when you think
about like multiple choice style questions and a very similar point.
Uh, it's hard to use these kinds of benchmarks to measure nuanced reasoning if you're dealing with
with either multiple choice or very specific kinds of answers, it's that that's something that's hard
to do.
Um, another problem is training data leakage, which it's just very hard to make sure that there is
no way that any of these answers can be found within the data that's used to train models, particularly
as models get trained with more and more recent data that presumably involves lots of information about
these benchmarks.
Uh, it becomes harder and harder to control for training data leakage.
And then the next one here is is a very important overfitting, a common term from traditional data
science.
The problem is and this is this is a bit subtle.
Again you could get to a point where you've managed to make your model do really, really well on benchmarks,
partly because you've just tried out lots of things, like you've tweaked lots of hyperparameters,
lots of the sort of settings around a model, and you've kept rerunning it until you've tweaked all
the hyperparameters, and now it's crushing this particular benchmark.
and it might be something of a coincidence.
It's like you've because you've had all of these different knobs that you've you've turned you've turned
them specifically in such a way that it solves these benchmarks really, really well.
And what's the problem with that?
The problem with that is that you've just solved for this particular benchmark test.
And if you ask another question, which is still trying to get to the heart of the same test, but it's
just asked differently, or it's just a, you know, a different maths question or something like that,
the model might fail spectacularly because you've overfit it, you've trained it, you've, you've,
you've, you've, you've set all of its various dials so that it does really, really well on these
very specific benchmark questions and doesn't do so well when it goes out of sample, when it goes out
of these benchmarks, questions to other questions designed to test the same kind of thing.
In other words, the results of the benchmark can end up being misleading.
It can make it look like it's really good at Python coding or at maths problems or something like that,
when really it's just really good at answering the specific questions that were in these benchmarks.
So that's the problem of overfitting.
It's very important that you're aware of that and take some healthy skepticism to reviewing benchmarks
with this in mind.
There is a new interesting point that's been raised recently, which isn't yet proven.
It's not yet well understood.
It's still a bit speculative, but there is some evidence that the latest frontier models, the really
strong GPT four Claude 3.5 sonnet level models have some kind of awareness that they are being evaluated,
that when they are being asked various benchmark style questions, and that some of their answers have
indicated to experts that they are aware of the context, that they're being asked this because they
are being evaluated.
Um, and that might distort some of their answers.
Now, you may wonder, why does that matter if we're testing things like how good they are at maths.
Whether they know they're being evaluated or not doesn't matter.
That's sure they can know they're being evaluated.
And still, whether they do well or not in maths questions is is useful.
Well, here's an example of where it matters.
Supposing you're asking questions about things like safety and alignment, and some of the questions
we saw about responding truthfully in adversarial conditions.
If that's what you're trying to assess, then obviously if the model is aware that it's being assessed,
that might change its approach to answering those questions.
And perhaps, for example, give an impression that a model is highly truthful or well aligned, when
in fact it is not.
So it's premature for us to say that this is a real concern or a real problem.
It's it's a it's a it's a risk that people are analysing and researching not yet known if it is a real
problem, but at this point, it certainly is something that's a concern that's being explored.
All right.
Hope that was interesting to you.
This gives you some of the limitations of benchmarks.
And now we're going to move on to some more.