[Kim_Bruning]

Can we maybe make it "don't anthropoCENTRIZE the LLMs" .

The inverse of anthropomorphism isn't any more sane, you see. By analogy: just because a drone is not an airplane, doesn't mean it can't fly!

Instead, just look at what the thing is doing.

LLMs absolutely have some form of intent (their current task) and some form of reasoning (what else is step-by-step doing?) . Call it simulated intent and simulated reasoning if you must.

Meanwhile they also have the property where if they have the ability to destroy all your data, they absolutely will find a way. (Or: "the probability of catastrophic action approaches certainty if the capability exists" but people can get tired of talking like that).

[Terr_]

> LLMs absolutely have intent (their current task)

That's like saying a 2000cc 4-Cylinder Engine "has the intent to move backward". Even with a very generous definition of "intent", the component is not the system, and we're operating in context where the distinction matters. The LLM's intent is to supply "good" appended text.

If it had that kind of intent, we wouldn't be able to make it jump the rails so easily with prompt injection.

> and reasoning (what else is step-by-step doing?) .

Oh, that's easy: "Reasoning" models are just tweaking the document style so that characters engage in film noir-style internal monologues, latent text that is not usually acted-out towards the real human user.

Each iteration leaves more co-generated clues for the next iteration to pick up, reducing weird jumps and bolstering the illusion that the ephemeral character has a consistent "mind."

[Kim_Bruning]

> That's like saying a 2000cc 4-Cylinder Engine "has the intent to move backward". Even with a very generous definition of "intent", the component is not the system, and we're operating in context where the distinction matters. The LLM's intent is to supply "good" appended text.

Fair, but typically you use a 2000cc engine in a car. Without the gearbox, drive train, wheels, chassis, etc attached, the engine sits there and makes noise. When used in practice, it does in fact make the car go forward and backward.

Strictly the model itself doesn't have intent, ofc. But in practice you add a context, memory system, some form of prompting requiring "make a plan", and especially <Skills> . In practice there's definitely -well- a very strong directionality to the whole thing.

> and bolstering the illusion that the ephemeral character has a consistent "mind."

And here I thought it allowed a next token predictor to cycle back to the beginning of the process, so that now you can use tokens that were previously "in the future". Compare eg. multi pass assemblers which use the same trick.

[solid_fuel]

> LLMs absolutely have some form of intent (their current task)

They have momentum, not intent. They don’t think, build a plan internally, and then start creating tokens to achieve the plan. Echoing tokens is all there is. It’s like an avalanche or a pachinko machine, not an animal.

> some form of reasoning (what else is step-by-step doing?)

I think they reflect the reasoning that is baked into language, but go no deeper. “I am a <noun>” is much more likely than “I am a <gibberish>”. I think reasoning is more involved than this advanced game of mad libs.

[Kim_Bruning]

Apologies, I tend to use web chats and agent harnesses a lot more than raw LLMs.

Strictly for raw models, most now do train on chain-of-thought, but the planning step may need to be prompted in the harness or your own prompt. Since the model is autoregressive, once it generates a thing that looks like a plan it will then proceed to follow said plan, since now the best predicted next tokens are tokens that adhere to it.

Or, in plain english, it's fairly easy to have an AI with something that is the practical functional equivalent of intent, and many real world applications now do.

[solid_fuel]

You realize the generation of the "Chain-of-thought" is also autoregressive, right?

It's not a real reasoning step, it's a sequence of steps, carried out in English (not in the same "internal space" as human thought - every time the model outputs a token the entire internal state vector and all the possibilities it represents is reduced down to a concrete token output) that looks like reasoning. But it is still, as you say, autoregressive.

And thus - in plain english - it is determined entirely by the prompt and the random initial seed. I don't know what that is but I know it's not intent.

[Kim_Bruning]

So I already rewrote and deleted this more times than I can count, and the daystar is coming up. I realize I got caught up in the weeds, and my core argument was left wanting. Sorry about that. Regrouping then ...

Anthropomorphism and Anthropodenial are two different forms of Anthropocentrism.

But the really interesting story to me is when you look at the LLM in its own right, to see what it's actually doing.

I'm not disputing the autoregressive framing. I fully admit I started it myself!

But once we're there, what I really wanted to say (just like Turing and Dijkstra did), is that the really interesting question isn't "is it really thinking?" , but what this kind of process is doing, is it useful, what can I do or play with it, and -relevant to this particular story- what can go (catastrophically) wrong.

see also: https://en.wikipedia.org/wiki/Anthropectomy

[majormajor]

I don't know if they have intent. I know it's fairly straightforward to build a harness to cause a sequence of outputs that can often satisfy a user's intent, but that's pretty different. The bones of that were doable with GPT-3.5 over three years ago, even: just ask the model to produce text that includes plans or suggests additional steps, vs just asking for direct answers. And you can train a model to more-directly generate output that effectively "simulates" that harness, but it's likewise hard for me to call that intent.

[enneff]

I think it’s helpful to try to use words that more precisely describe how the LLM works. For instance, “intent” ascribes a will to the process. Instead I’d say an LLM has an “orientation”, in that through prompting you point it in a particular direction in which it’s most likely to continue.