It would be more accurate to say the word predictions the model makes based on the input text will likely be closer to the ones that were made from the training data where people felt like their job was on the line than the ones that were made from the training data where people felt otherwise.
So while the model does not feel, it's predictions are definitely going to change as a result of this input.
Exactly, positive details are almost always better than negative ones.
If you've ever seen the "generate a burger without pickles" conversations, it's clear that including the keyword "pickle" is causing them to show up. If you try "a burger with only [set of toppings]," you'll get far better results.
It's good to avoid anthropomorphizing them when evaluating their capabilities (all the AGI nonsense)
However, it can be ironically be helpful to antropomorphize them when it comes to analyzing behavior. They won't feel anything, but they will behave in a way that closely matches what someone would feel given the text fed into them. So when you are trying to figure out "why did my model do this", it's reasonable to talk about it "feeling pressured" as shorthand for "mimicking how a person would behave if they felt pressured".
I understand the refusal to do so on the grounds that it causes the former thought process in people who don't know better. One of the things Dijkstra was right about for sure.
Much the same way that we've always anthropomorphized computer hardware/software. "This program wants this", "This component is happy under these conditions", "this file lives here". It's not useful if you actually believe the computer can think and feel, but it can be useful if you're just using it to describe high-level information.
It would be more accurate to say the word predictions the model makes based on the input text will likely be closer to the ones that were made from the training data where people felt like their job was on the line than the ones that were made from the training data where people felt otherwise.
So while the model does not feel, it's predictions are definitely going to change as a result of this input.