[Jensson]

It has nothing to do with ratio and to do with intent. Bullshitting is what we say you do when you just spin a story with no care for the truth, just make up stuff that sound plausible. That is what LLMs do today, and what they will always do as long as we don't train them to care about the truth.

You can have a generative model that cares about the truth when it tries to generate responses, its just the current LLMs don't.

[Ma8ee]

> You can have a generative model that cares about the truth when it tries to generate responses, its just the current LLMs don't.

How would you do that, when they don’t have any concept of truth to start with (or any concepts at all).

[Jensson]

You can program a concept of truth into them, or maybe punishing it for making mistakes instead of just rewarding it for replicating text. Nobody knows how to do that in a way that get intelligent results today, but we know how to code things that outputs or checks truths in other contexts, like wolfram alpha is capable of solving tons of things and isn't wrong.

> (or any concepts at all).

Nobody here said that, that is your interpretation. Not everyone who is skeptical of current LLM architectures future potential as AGI thinks that computers are unable to solve these things. Most here who argues against LLM don't think the problems are unsolvable, just not solvable by the current style of LLMs.

[Ma8ee]

> You can program a concept of truth into them, ...

The question was, how you do that?

> Nobody here said that, that is your interpretation.

What is my interpretation?

I don't think that the problems are unsolvable, but we don't know how to do it now. Thinking that "just program the truth in them" shows a lack of understanding of the magnitude of the problem.

Personally I'm convinced that we'll never reach any kind of AGI with LLM. They are lacking any kind of model about the world that can be used to reason about. And the concept of reasoning.

[Jensson]

> The question was, how you do that?

And I answered, we don't know how you do that which is why we don't currently.

> Personally I'm convinced that we'll never reach any kind of AGI with LLM. They are lacking any kind of model about the world that can be used to reason about. And the concept of reasoning.

Well, for some definition of LLM we probably could. But probably not the way they are architected today. There is nothing stopping a large language model to add different things to its training steps to enable new reasoning.

> What is my interpretation?

Well, I read your post as being on the other side. I believe it is possible to make a model that can reason about truthiness, but I don't think current style LLMs will lead there. I don't know exactly what will take us there, but I wouldn't rule out an alternate way to train LLMs that looks more like how we teach students in school.

[mistermann]

Key words like "epistemology" in the prompt. Chat GPT generally outperforms humans in epistemology substantially in my experience, and it seems to "understand" the concept much more clearly and deeply, and without aversion (lack of an ego or sense of self, values, goals, desires, etc?).

[almostgotcaught]

> It has nothing to do with ratio and to do with intent. Bullshitting is what we say you do when you just spin a story with no care for the truth, just make up stuff that sound plausible

Do you people hear yourselves? You're discussing the state of mind of a pseudo-RNG...

[Jensson]

ML models intent is the reward function it has. They strive to maximize rewards, just like a human does. There is nothing strange about this.

Humans are much more complex than these models so they have much more concepts and stuff which is why we need psychology. But some core aspects works the same in ML and in human thinking. In those cases it is helpful to use the same terminology for humans and machine learning models, because that helps transfer understanding from one domain to the other.