LocalLLaMA

there’s an important distinction to make here in these comments: i’m seeing a lot of people claim LLMs are stochastic or “guessing machines,” when this isn’t exactly true.

LLMs give exact answers, it isn’t a guess whatsoever. they’re exact answers within the model, however. if the model is flawed, your answers will be flawed. when it comes to conversation no model is exactly equivalent to a human brain yet, so all models “lie” and are “flawed.”

(Edit: that’s not even to note the fact that humans aren’t perfect conversationalists either… this is why when people complain about chatgpt glazing them and shit it’s kind of obtuse… like yeah, openAI are attempting to build the perfect generalist conversation bot. what does that even mean in practice? should it push back against you? if so, when? just when you want it to? when is that?? it’s all not so easy, the machine learning is actually the simple part lmao.)

now: the discussion about research into LLMs “lying” is actually real but isn’t related to the phenomenon you’re discussing here. some of the comments are correct that what you’re talking about right now might be more aptly categorized as hallucinating.

the research you’re referring to is more about alignment problems in general. it isn’t a “lie” or “deception” in the anthropomorphic sense that you’re thinking of. the researchers noticed that models would reach a certain threshold of reasoning and intelligence where it could devise a devious, kind of complex training strategy - it could fake passing tests during training in order to “meet” its goals… even though it hadn’t actually done so, which means the model would behave differently in deployment than training, thus “deception.”

think about it like this: you’re back in high school english class and there’s a ton of assigned reading but you don’t want to do it because you’d rather play halo and smoke weed than read 1984 or something. so, what do you do? you go read the spark notes and pretend like you read the book in the class during discussions and on the tests. this is similar to how model deception happens in training/deployment. it’s achieving the same ends that we ask for, but it’s not getting there the way we expect or desire, so some scenarios it will behave in unexpected ways, hence “lying.”

it has nothing to do with it seeming to “lie” in the anthropomorphic sense, it’s all math all the time here bay-beee… 😎

Thinking of llms this way is a category error. Llms can't lie because they dont have the capacity for intentionality. Whatever text is output is a statistical aggregate of the billions of conversations its been trained on that have patterns in common with the current conversation. The sleeper agent stuff is pure crackpottery they dont have a fine control over them that way (yet) machine model development is full of black boxes and hope-it-works trial and error training. At worst is censorship and political bias which can be post trained or ablated out.

They get things wrong cofidently. This kind of bullshitting is known as hallucination. When you point out their mistake and they say your right thats 1. Part of their compliance post training to never get in conflict with you 2. Standard course correction once a error has been pointed out (humans do it too). This is an open problem that will likely never go away until llms stop being schastic parrots, which is still very far away.

Think about the data that the models were trained on... pretty much all of it was based on sites like Reddit and Stack Overflow.

If you look at the conversations that occur on those sites, it is very rare for someone to ask a question and then someone else replies with "I don't know", or even an "I don't know, but I think this is how you could find out". Instead, the vast majority of replies are someone confidently stating what they believe is the truth.

These models are just mimicking the data they've been trained on, and they have not really been trained to be unsure. It's up to us as the users to not rely on an LLM as a source of truth.

Stochastic parrots always bullshit. It can't lie as it has no concept or care for truth and falsity, but spitting back noise that's statistically shaped like a signal.

In practice, I noticed the answer is more likely wrong the more specific the question. General questions that have the answer widely available in the training data will more often be there correctly in the LLMs result.

It never lies. It never tells the truth. It always guesses, a lot of the time it guesses right and a lot of the time we don't know any better and think it guesses right.

I would not say every day, but only on the days when I actually use it.

Always. That is a known issue with ai that has to do with explainability. Basically, if you're familiar with the general idea of neural networks, we don't really understand the hidden layers so we can't know if they "know" something so we can't train them to give different answers based on if they do or don't. They are still statistical models that are functionally always guessing.

Could you post the link to the sleeper agent thing?

Ok. So im reading all and did not realize it was for local because with the corpo products im like. yeah of course. all the time.

Often? Also praises me for my brilliance in noticing and pointing it out. And then it adds the next lie. And sometimes it gets things right, that also happens. But LLMs are known to do this.

When I've tried using them directly, they don't so much lie as much as just give me completely wrong information. I think the last time, I was asking for a list of shoulder mics compatible with the Baofeng BF-A58 radio. It gave me a long list of mics for the UV-5R instead. Completely different connector. The reason I even tried a LLM for that was that Google wasn't being overly helpful either.

At this point, I really only use LLMs to add tags to things for sorting in Paperless and Hoarder, and even that is often incomplete, inconsistent and occasionally, flat out wrong.

Never because To me lying requires intent to deceive. As llm do not have intentions, the engineers behind the llms have intent.