Large language artificial intelligence models, such as ChatGPT, often misjudge what people outside the West might value as a moral priority, according to our new research published in the Proceedings of the National Academy of Sciences.
Large language artificial intelligence models, such as ChatGPT, often misjudge what people outside the West might value as a moral priority, according to our new research published in the Proceedings of the National Academy of Sciences.
This is dumb. LLMs do not reason and don’t prioritize viewpoints, they determine the order of a string of words statistically based on context given to them directly, generally in the form of a query.
If you anthropomorphize the LLM, you make it seem like it’s making decisions when it’s not.
It’s not anthropomorphism, it’s explaining a bias in their output based on training data.
That’s like the definition of training data.
All output of all kinds is biased by the training data.
I’m not debating what is or isn’t training data, I’m saying it’s not anthropomorphism to say an LLM has a bias.
How is that anything but anthropomorphism?
It helps to read the rest of the article which explains the methodology behind that thesis.
Besides, how is it anthropomorphism? Is there any other way to convey that point?
Don’t use words like misjudge, prioritize or overlook? They’re not doing any of those.
I don’t agree that those terms obfuscate what AI is when taken in context of the whole article.
I’m honestly asking how to get that point across in other terms, do you have an example of what would work better?
“LLMs output may be biased by their input data and users should be careful to account for this when using them.”