By using and testing it, obviously. It can’t magically develop a bias later on.
Everyone has a different definition of what unbiased means, so this would not be a “one size fits all” kind of thing. You would simply use a model that you personally deem good enough.
Not obvious. You’re right, there is no magic in this technology, and you clearly don’t understand how it works.
There is not a single LLM currently available that is able to consistently provide a correct or workable solution when faced with a semi complex word problem that’s able to be contained in one paragraph. They may nail it on occasion but they cannot do it consistently. The “problem” of figuring out if someone is a bit eccentric, has poor social skills, is actively trolling, only trolls sometimes, or any combination of the above, is orders of magnitude higher than that. (edit:) To say an LLM is capable of that kind of logical determination is completely ignoring the evidence to the contrary.
I disagree, parsing through buckets of text (not one paragraph, a user’s full comment history) is literally the only thing LLMs ARE good at. This is not a logic problem, nor is it something that requires 100% accuracy.
It doesn’t matter if someone is just weird or malicious, I don’t necessarily want to engage in dialog with someone who is unlikely to respect my time or words.
Right, if we’re talking about “good” in regards to speed then you’re correct. But if we’re talking about discerning intent, seriously? I find it hard to believe you’re speaking in good faith and without bias yourself here. Disguising intent is the leading method to ‘jailbreak’ an LLM. Half the time at least, trolls are attempting to disguise their intent (with varying degrees of success). So that would be a solid failure at worst, or miss swaths of trolls at best.
I don’t want to engage with someone like that either, but I care about not skipping over the people on the fringes of behavior, people who don’t just regurgitate an echo chamber. This task might not require 100% accuracy but I personally wouldn’t be satisfied with anything less than 99.9%.
I think using something like what we’ve been talking about is very very very far off in the future for me, if I were to ever do so at all. This conversation has made me realize that.
That… doesn’t answer the question. How do you assert that the base model you download and run doesn’t have a bias one way or the other?
By using and testing it, obviously. It can’t magically develop a bias later on.
Everyone has a different definition of what unbiased means, so this would not be a “one size fits all” kind of thing. You would simply use a model that you personally deem good enough.
Not obvious. You’re right, there is no magic in this technology, and you clearly don’t understand how it works.
There is not a single LLM currently available that is able to consistently provide a correct or workable solution when faced with a semi complex word problem that’s able to be contained in one paragraph. They may nail it on occasion but they cannot do it consistently. The “problem” of figuring out if someone is a bit eccentric, has poor social skills, is actively trolling, only trolls sometimes, or any combination of the above, is orders of magnitude higher than that. (edit:) To say an LLM is capable of that kind of logical determination is completely ignoring the evidence to the contrary.
I disagree, parsing through buckets of text (not one paragraph, a user’s full comment history) is literally the only thing LLMs ARE good at. This is not a logic problem, nor is it something that requires 100% accuracy.
It doesn’t matter if someone is just weird or malicious, I don’t necessarily want to engage in dialog with someone who is unlikely to respect my time or words.
Right, if we’re talking about “good” in regards to speed then you’re correct. But if we’re talking about discerning intent, seriously? I find it hard to believe you’re speaking in good faith and without bias yourself here. Disguising intent is the leading method to ‘jailbreak’ an LLM. Half the time at least, trolls are attempting to disguise their intent (with varying degrees of success). So that would be a solid failure at worst, or miss swaths of trolls at best.
I don’t want to engage with someone like that either, but I care about not skipping over the people on the fringes of behavior, people who don’t just regurgitate an echo chamber. This task might not require 100% accuracy but I personally wouldn’t be satisfied with anything less than 99.9%.
I think using something like what we’ve been talking about is very very very far off in the future for me, if I were to ever do so at all. This conversation has made me realize that.
Fair enough! It was really a hypothetical anyway. No such system has been built to my knowledge.