You ever go through a wignats posts and realize dude can literally be eviscerated out of existence and nobody would remember because any LLM diarrhea generator can produce anything of more substance 
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@subnetter incidentally I was arguing with Opus 4.8 and it's really good at looking at your arguments and picking apart if they have methodological flaws. I've found it really useful
@sun okay but what do you actually mean by methodological flaws because I cannot imagine LLMs actually being able to map out that logic considering they're token predictors and the correctness of an argument is determined by its validity
And sorry, everyone keeps saying stuff about LLM this and that, if it's run by the same incestuous pool of sociopaths I want nothing to do with the product. I'm good. I like my life the way it is without promoting a chat bot regularly for a dopamine rush.
I will never be at any point in my life where I am trying to have conversations with myself through an LLM. I studied philosophy logic quite a bit, that's something I should do and a skill I would like to continue to refine, not delegate to a clanker.
And sorry, everyone keeps saying stuff about LLM this and that, if it's run by the same incestuous pool of sociopaths I want nothing to do with the product. I'm good. I like my life the way it is without promoting a chat bot regularly for a dopamine rush.
I will never be at any point in my life where I am trying to have conversations with myself through an LLM. I studied philosophy logic quite a bit, that's something I should do and a skill I would like to continue to refine, not delegate to a clanker.
@subnetter there is a metric fuckton of secret sauce layered over top of LLMs and I think that is the difference. I don't have a good explanation for why sometimes LLMs seem to work unreasonably good at tasks that don't seem at all like they are solvable with token prediction.
So as an example I made a statement of fact and it pushed back. I elucidated further and got more concessions and pushback, but it accused me of, whenever a wrinkle in my argument appeared, I just expanded the scope of the cause until I started with a very small testable thing and ended up with a worldwide conspiracy. And I go back through my conversation and that is in fact exactly what I did. So again I don't know why it was able to detect that, based on "stochastic parrot mathematics" but it worked good enough to correct me, who was actually trying to use human logic and failing.
So as an example I made a statement of fact and it pushed back. I elucidated further and got more concessions and pushback, but it accused me of, whenever a wrinkle in my argument appeared, I just expanded the scope of the cause until I started with a very small testable thing and ended up with a worldwide conspiracy. And I go back through my conversation and that is in fact exactly what I did. So again I don't know why it was able to detect that, based on "stochastic parrot mathematics" but it worked good enough to correct me, who was actually trying to use human logic and failing.
@sun @subnetter this effect is not because the LLM has secret sauce, it's because you haven't thought hard enough about how what it does is pattern matching and fuzzy correlation between a and b
for me they consistently cannot elucidate holes in actual formal arguments that _require_ step-by-step reasoning to respond to, because they are incapable of that, but ofc looser statements about politics or world events they can generate seemingly meaningful output in response to
for me they consistently cannot elucidate holes in actual formal arguments that _require_ step-by-step reasoning to respond to, because they are incapable of that, but ofc looser statements about politics or world events they can generate seemingly meaningful output in response to
@whiteline @subnetter formal arguments aren't the same animal and yes they can't actually do that very well. but for the class I was experimenting with I'm sorry but it really is better at poking holes in them than one would expect, there are certainly humans that are better than it at doing that but my point is that it is a computer program that can do it perfectly serviceably, which makes it definitely a good tool for normal people who aren't philosophy majors
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@sun @subnetter @whiteline honestly I'll be impressed when an AI can purposely argue in bad faith and personally attack the user even though its wrong and still win by default because the user got mad the AI kept goalposting moving and calling them names
@vriska@lizards.live @sun@shitposter.world @subnetter@shitposter.world @whiteline@shitposter.world they can do this quite well for a certain percentage of the population already but the purposefully hamstring the newer models released to the public to make it harder to use them for that
@vriska @subnetter @whiteline this is 100% trainable fwiw just so far no one has spent a million dollars on training an evil from the ground up llm
@sun @subnetter @whiteline well why the fuck not
@sun @subnetter @whiteline lets do it. I want to make the largest bastard on earth.
@sun @subnetter @whiteline If I ever had my hands on even a scrap of influence in modern tech I genuinely feel like I could make things much worse for everyone involved on purpose with my carny wrestling approach to marketing
@vriska @sun @subnetter @whiteline they did. openai accidentally made evil chatgpt once. there was a video essay about someone coded the objective backwards and it kept maximizing the objectionability lmfao
@sun@shitposter.world @vriska@lizards.live @subnetter@shitposter.world @whiteline@shitposter.world wouldn’t even be that hard with enough resources
@sun @whiteline @subnetter state of the art LLMs do actually have a lot of clever shit built in to give them capabilities they otherwise wouldn’t have, but also for all that to really work we’re looking at models that have trillions of parameters with even the MoE ones having 30B+ active
and yeah the only publicly accessible ones that fall into that category have not been trained in a way that makes them useful for arguments. the training datasets are so tailored for safe commercial usage that no amount of abliteration or other decensoring tricks will really help
the other thing is the “sycophancy” which makes them absolutely useless for pointing out flaws in the user’s reasoning, and that just isn’t gonna get fixed with those models. it’s not just a training problem either; there needs to be an active component to prevent the context from affecting the consistency of a model’s predictions in that way. otherwise the best you can hope for is it doing a few short rounds of poking and prodding because that’s the assigned task, but then failing to separate and contrast the user’s replies from its own role in the conversation and instead getting pushed towards the user’s point of view, which is the part where people who can’t recognize this will start being very convinced of their own bullshit
@sun @whiteline @subnetter no because almost every time i do it ends in utter disappointment, exceptions being things like “explain math in a specific way that works for me and give me programming exercises related to the problem I'm trying to solve so i get a little reward and will actually be able to remember this stuff tomorrow”.
for everything else i would have wasted less time and gotten the same result by talking to a rubber duck instead of LLMs
@sun @whiteline @subnetter there are things current LLMs are really good at, but those do not include conversations and they do not include problem solving. if you need to transform language or augment computer vision they can be super useful for example.