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.
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 might be the worst idea I’ve ever heard in my life.
As with all people who are apparently experiencing AI psychosis I highly suggest you just learn to do things for yourself. You can make your own tags based on your own observations and experiences.
I think a cool solution would be to aggregate all of the tags that each user has received from other users and if there are frequent enough overlaps, a suggested tag might show up next to their name.
Of course, that would require user tags to be logged, which is not currently the case, afaik. It’s also not foolproof, because I’ve got at least one user tagged as “belligerent and stupid,” which, while probably helpful for others, is not likely to come up in other users’ tags. Most of my tags are probably pretty common though: troll, occasional troll, thoughtful, insightful, etc.
And finally, it might be susceptible to brigading or worse, if someone decides to make a bunch of accounts to tag LGBTQ users or something. Using the same federation rules as in other scenarios, where users or instances can be blocked or defederated at the instance level would help that, but I don’t know if it’s possible.
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Yeah but how would you trust that the LLM isn’t biased, or the company that licenses and puts it in a browser extension isn’t either? I don’t know.
I’m asking because I like the idea, it’s a good one.
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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?
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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.
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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.
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That might be the worst idea I’ve ever heard in my life.
As with all people who are apparently experiencing AI psychosis I highly suggest you just learn to do things for yourself. You can make your own tags based on your own observations and experiences.
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Yes you’re right that’s certainly does sound like a reason why we shouldn’t have any more clean water. Totally worth it.
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I thought it was a good idea
I think a cool solution would be to aggregate all of the tags that each user has received from other users and if there are frequent enough overlaps, a suggested tag might show up next to their name.
Of course, that would require user tags to be logged, which is not currently the case, afaik. It’s also not foolproof, because I’ve got at least one user tagged as “belligerent and stupid,” which, while probably helpful for others, is not likely to come up in other users’ tags. Most of my tags are probably pretty common though: troll, occasional troll, thoughtful, insightful, etc.
And finally, it might be susceptible to brigading or worse, if someone decides to make a bunch of accounts to tag LGBTQ users or something. Using the same federation rules as in other scenarios, where users or instances can be blocked or defederated at the instance level would help that, but I don’t know if it’s possible.