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A non-anthropomorphized view of LLMs

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Re: A non-anthropomorphized view of LLMs

#191

Post by quantumgarbage »

NetRunnerSu wrote: post #123

The author's critique of naive anthropomorphism is salient. However, the reduction to "just MatMul" falls into the same trap it seeks to avoid: it mistakes the implementation for the function. A brain is also "just proteins and currents," but this description offers no explanatory power. The correct level of analysis is not the substrate (silicon vs. wetware) but the computational principles being executed. A modern…

> A modern sparse Transformer, for instance, is not "conscious," but it is an excellent engineering approximation of two core brain functions: the Global Workspace (via self-attention) and Dynamic Sparsity (via MoE).

Could you suggest some literature supporting this claim? Went through your blog post but couldn't find any.

Re: A non-anthropomorphized view of LLMs

#192

Post by LeonardoTolstoy »

flir wrote: post #154
cmenge wrote: post #126

Earlier quoted context omitted.

I kinda agree with both of you. It might be a required abstraction, but it's a leaky one. Long before LLMs, I would talk about classes / functions / modules like "it then does this, decides the epsilon is too low, chops it up and adds it to the list". The difference I guess it was only to a technical crowd and nobody would mistake this for anything it wasn't. Everybody know that "it" didn't "decide" anything. With AI…

Agreeing with you, this is a "can a submarine swim" problem IMO. We need a new word for what LLMs are doing. Calling it "thinking" is stretching the word to breaking point, but "selecting the next word based on a complex statistical model" doesn't begin to capture what they're capable of. Maybe it's cog-nition (emphasis on the cog).

What does a submarine do? Submarine? I suppose you "drive" a submarine which is getting to the idea: submarines don't swim because ultimately they are "driven"? I guess the issue is we don't make up a new word for what submarines do, we just don't use human words.

I think the above poster gets a little distracted by suggesting the models are creative which itself is disputed. Perhaps a better term, like above, would be to just use "model". They are models after all. We don't make up a new portmanteau for submarines. They float, or drive, or submarine around.

So maybe an LLM doesn't "write" a poem, but instead "models a poem" which maybe indeed take away a little of the sketchy magic and fake humanness they tend to be imbued with.

Re: A non-anthropomorphized view of LLMs

#193

Post by CuriousSkeptic »

tempfile wrote: post #184
Al-Khwarizmi wrote: post #117

I have the technical knowledge to know how LLMs work, but I still find it pointless to not anthropomorphize, at least to an extent. The language of "generator that stochastically produces the next word" is just not very useful when you're talking about, e.g., an LLM that is answering complex world modeling questions or generating a creative story. It's at the wrong level of abstraction, just as if you were discussing…

The "point" of not anthropomorphizing is to refrain from judgement until a more solid abstraction appears. The problem with explaining LLMs in terms of human behaviour is that, while we don't clearly understand what the LLM is doing, we understand human cognition even less! There is literally no predictive power in the abstraction "The LLM is thinking like I am thinking". It gives you no mechanism to evaluate what ta…

> Why don't LLMs get frustrated with you if you ask them the same question repeatedly?

To be fair, I have had a strong sense of Gemini in particular becoming a lot more frustrated with me than GPT or Claude.

Yesterday I had it ensuring me that it was doing a great job, it was just me not understanding the challenge but it would break it down step by step just to make it obvious to me (only to repeat the same errors, but still)

I’ve just interpreted it as me reacting to the lower amount of sycophancy for now

Re: A non-anthropomorphized view of LLMs

#194

Post by hackinthebochs »

positron26 wrote: post #103
lostmsu wrote: post #101

Earlier quoted context omitted.

> Current LLMs can only introspect from output tokens The only interpretation of this statement I can come up with is plain wrong. There's no reason LLM shouldn't be able to introspect without any output tokens. As the GP correctly says, most of the processing in LLMs happens over hidden states. Output tokens are just an artefact for our convenience, which also happens to be the way the hidden state processing is tra…

There are no recurrent paths besides tokens. How may I introspect something if it is not an input? I may not.

Important attention heads or layers within an LLM can be repeated giving you an "unrolled" recursion.

Re: A non-anthropomorphized view of LLMs

#195

Post by gugagore »

NiloCK wrote: post #90
gpm wrote: post #86

Earlier quoted context omitted.

The LLM does not "have" a plan. Arguably there's reason to believe it comes up with a plan when it is computing token propabilities, but it does not store it between tokens. I.e. it doesn't possess or "have" it. It simply comes up with a plan, emits a token, and entirely throws all its intermediate thoughts (including any plan) to start again from scratch on the next token.

I don't think that the comment above you made any suggestion that the plan is persisted between token generations. I'm pretty sure you described exactly what they intended.

The concept of "state" conveys two related ideas.

- the sufficient amount of information to do evolution of the system. The state of a pendulum is it's position and velocity (or momentum). If you take a single picture of a pendulum, you do not have a representation that lets you make predictions.

- information that is persisted through time. A stateful protocol is one where you need to know the history of the messages to understand what will happen next. (Or, analytically, it's enough to keep track of the sufficient state.) A procedure with some hidden state isn't a pure function. You can make it a pure function by making the state explicit.

Re: A non-anthropomorphized view of LLMs

#196

Post by tliltocatl »

amelius wrote: post #140
grey-area wrote: post #120

Earlier quoted context omitted.

On the contrary, anthropomorphism IMO is the main problem with narratives around LLMs - people are genuinely talking about them thinking and reasoning when they are doing nothing of that sort (actively encouraged by the companies selling them) and it is completely distorting discussions on their use and perceptions of their utility.

I don't agree. Most LLMs have been trained on human data, so it is best to talk about these models in a human way.

Anthropomorphising implicitly assumes motivation, goals and values. That's what the core of anthropomorphism is - attempting to explain behavior of a complex system in teleological terms. And prompt escapes make it clear LLMs doesn't have any teleological agency yet. Whenever their course of action is, it is to easy to steer them of. Try to do it with a sufficiently motivated human.

Re: A non-anthropomorphized view of LLMs

#197

Post by hackinthebochs »

cuttothechase wrote: post #91
ants_everywhere wrote: post #20

> I am baffled that the AI discussions seem to never move away from treating a function to generate sequences of words as something that resembles a human. This is such a bizarre take. The relation associating each human to the list of all words they will ever say is obviously a function. > almost magical human-like powers to something that - in my mind - is just MatMul with interspersed nonlinearities. There's a ric…

>Think of it this way: do you believe there's anything about humans that exists outside the mathematical laws of physics? If so that's essentially a religious position (or more literally, a belief in the supernatural). If not, then functions and approximations to functions are what the human experience boils down to. It seems like, we can at best, claim that we have modeled the human thought process for reasoning/ana…

>Why should we expect the model to be anything more than a model ?

To model a process with perfect accuracy requires recovering the dynamics of that process. The question we must ask is what happens in the space between bad statistical model and perfect accuracy? What happens when the model begins to converge towards accurate reproduction. How far does generalization in the model take us towards capturing the dynamics involved in thought?

Re: A non-anthropomorphized view of LLMs

#198

Post by positron26 »

hackinthebochs wrote: post #194
positron26 wrote: post #103

Earlier quoted context omitted.

There are no recurrent paths besides tokens. How may I introspect something if it is not an input? I may not.

Important attention heads or layers within an LLM can be repeated giving you an "unrolled" recursion.

An unrolled loop in a feed-forward network is all just that. The computation is DAG.

Re: A non-anthropomorphized view of LLMs

#199

Post by gugagore »

barrkel wrote: post #175
gugagore wrote: post #39

Earlier quoted context omitted.

That's not what "state" means, typically. The "state of mind" you're in affects the words you say in response to something. Intermediate activations isn't "state". The tokens that have already been generated, along with the fixed weights, is the only data that affects the next tokens.

Sure it's state. It logically evolves stepwise per token generation. It encapsulates the LLM's understanding of the text so far so it can predict the next token. That it is merely a fixed function of other data isn't interesting or useful to say. All deterministic programs are fixed functions of program code, inputs and computation steps, but we don't say that they don't have state. It's not a useful distinction for…

I'll say it once more: I think it is useful to distinguish between autoregressive and recurrent architectures. A clear way to make that distinction is to agree that the recurrent architecture has hidden state, while the autoregressive one does not. A recurrent model has some point in a space that "encapsulates its understanding". This space is "hidden" in the sense that it doesn't correspond to text tokens or any other output. This space is "state" in the sense that it is sufficient to summarize the history of the inputs for the sake of predicting the next output.

When you use "hidden state" the way you are using it, I am left wondering how you make a distinction between autoregressive and recurrent architectures.

Re: A non-anthropomorphized view of LLMs

#200

Post by loxs »

cmenge wrote: post #126
grey-area wrote: post #120

Earlier quoted context omitted.

On the contrary, anthropomorphism IMO is the main problem with narratives around LLMs - people are genuinely talking about them thinking and reasoning when they are doing nothing of that sort (actively encouraged by the companies selling them) and it is completely distorting discussions on their use and perceptions of their utility.

I kinda agree with both of you. It might be a required abstraction, but it's a leaky one. Long before LLMs, I would talk about classes / functions / modules like "it then does this, decides the epsilon is too low, chops it up and adds it to the list". The difference I guess it was only to a technical crowd and nobody would mistake this for anything it wasn't. Everybody know that "it" didn't "decide" anything. With AI…

We can argue all day what "think" means and whether a LLM thinks (probably not IMO), but at least in my head the threshold for "decide" is much lower so I can perfectly accept that a LLM (or even a class) "decides". I don't have a conflict about that. Yeah, it might not be a decision in the human sense, but it's a decision in the mathematical sense so I have always meant "decide" literally when I was talking about a piece of code.

It's much more interesting when we are talking about... say... an ant... Does it "decide"? That I have no idea as it's probably somewhere in between, neither a sentient decision, nor a mathematical one.

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