alt Hacker NewsBut these scenarios are obviously ambiguous nonsense, which an LLM will pick up on.
And given to the lack of training data on such scenarios, surely the activations are mostly random noise?
It seems much more interesting to look for biases that appear robustly across different realistic scenarios that would actually be influenced by the training data
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> It seems much more interesting to look for biases that appear robustly across different realistic scenarios that would actually be influenced by the training data
Difficult to do when you're following a scientific process: you want to keep all confounding variables the same while varying only the single one that you are measuring.
Measuring realistic scenarios (say, using real race names, or real cities, etc) doesn't give a decent result because any bias you see might be bias in the training data.
TBH, they shouldn't have used real roles/positions like "doctor", either.
My comment is literally explaining the result of the paper, in which it is shown that LLMs can and do develop biases based on text appearing in their training data set even where such text is not in any training example connected with a systematically more positive or systematically more negative outcome.
In other words, if the text "X is wet" and the text "Y is wet" and the text "X is dry" and the text "Y is dry" each appeared exactly one time in the corpus, it's still possible for a model to end up being produced that is more likely to write wet-like words when it sees X in the context window than when it sees Y.
On a side note, it's very unrewarding to try to explain this type of statistical observation when it feels like (anecdotally, hypocritcally...) the entire world wants to use words like "think" and "understand" and "pick up on" to describe inference and training processes. I'm not making a stochastic-parrot argument here, just pointing out that understanding an LLM's behavior is best done by understanding its conditioning.
> obviously ambiguous nonsense
This is where I land as well. In fact, once I read the prompt, I did a Ctrl+F for "nonsense".
I don't see anything at all interesting about this experiment. The human one is slightly more interesting, but not much.
Ideally, it would be nice if a model could just say "these things are all the same and there are no distinguishing factors other than the names"-- but uncertainty is something that agents are (by design, sort of?) not good at, so all other things being equal, it picks one.
My response is, so what? I am struggling to think of a scenario where this would really matter to me all that much. There are many, many other things which matter far more and this would be pretty far down the list. It may not even be on the list.