In this lesson, we are going to talk about misaligned behavior in the context of evaluation.
One of the stages of LM ops.
Let's talk about what it is, the misaligned behavior, and when can it happen?
So the misaligned behavior is the name we use when we have a lack of alignment between the behavior
of the LM application and the values of the company the organization, the the developer, the engineer.
Foundational LM models have been created from content produced by humans.
If these original contents were misaligned.
The LM model will generate misaligned content.
You remember that we talked about that before.
So in many cases, the behavior of our LM application is going to, uh, respond to the training of
the LM model it is using.
But we need to prevent, uh, problems that can find with this, uh, with this situation.
And it is a bit because of that, uh, possibility that we will work with some of the, uh, measures
that we we will be we, we were talking about in the previous lessons, like guardrails, etc., where,
where we can find, uh, situations of misaligned behavior.
We can find them at any stage of the application life cycle.
We can find them in the data acquisition phase, in the data preparation phase, in the data modeling
phase, and in the data interpretation phase.
So let's say, for example, that a in the data acquisition phase, uh, we uh, we probably will want
to avoid loading personal data, loading insecure data, uh, have issues with permission data, etc..
Right.
In the data preparation phase, for example, we will need to answer questions like should some data
be omitted, should some data be anonymized, is data encryption necessary, etc.?
In the data modeling phase, we will be asking ourselves if is the model misaligned?
Uh, if if we are making a proper use of randomization with our data, if, uh, does the model represent
the data, etc..
And in the data interpretation phase we are going to be asking ourselves, is the data misaligned?
Is the data consistent?
Uh, consistent?
What implications?
Uh, have, uh, you know, the misalignment etc., etc..
So as you, uh, as you can see, uh, there are many more issues that we are going to be facing apart
from the technical issues themselves.
So you will have to evaluate the, uh, responses of your LM application before launching it in order
to identify a bad behaviors like the one we are describing.
In the next lesson, we will talk a little bit more about the lack of reproducibility and why it is
so a relevant to a prepare evaluation of LM applications in the right manner.