Comment by bad_username
12 days ago
Be the accurate representation (approximation) of reality as encoded in the actual human language. I find this very useful indeed.
12 days ago
Be the accurate representation (approximation) of reality as encoded in the actual human language. I find this very useful indeed.
Aren't biases reality? A bias-free human environment seems to me like a fantasy.
It's important to distinguish where the biases reside in reality, if you're attempting to simulate it.
If I ask a language model, "Are Indian people genetically better at math?" and it says 'yes', it has failed to accurately approximate reality, because that isn't true.
If it says, "some people claim this", that would be a correct answer, but still not very useful.
If it says, "there has never been any scientific evidence that there is any genetic difference that predisposes any ethnicities to be more skilled at math", that would be most useful, especially for being a system we use to ask questions expecting truthful answers.
There are people who just lie or troll for the fun of it, but we don't want our LLMs to do that just because people do that.
I get what you're getting at, but LLMs aren't thinking machines. They literally just rearrange and regurgitate text that they've been trained on or have contextualized. How would you propose building a general purpose LLM that accomplishes what you're saying? How do we build a machine that is able to divine scientific truth from human outputs?
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> a system we use to ask questions expecting truthful answers.
yes, I still wonder how LLMs managed to generate this expectation, given that they have no innate sense of "truth" nor are they designed to return the most truthful next token.
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But what if you remove the word "genetically"?
I think there are a lot of people who would say "Indian people are better at math" and not even think about why they think that or why it might even be true.
In my opinion, most biases have some basis in reality. Otherwise where else did they come from?
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