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Comment by D-Machine | Hacker News Reader

> Emergent behaviors in terms of seemingly random combinations of features producing unpredictable but coherent/intentional output basically don't exist.

You'll need to elaborate here, because Navier-Stokes plus Mathocalypse reactions are extremely strong evidence for coherent emergent behavior coming out of these models.

> The only thing emergent in LLMs is degenerate state leading to a breakdown in tracking relevant features of the input and a reduction in quality of output.

This is so provably wrong with two seconds of Googling / AI-asking that you must be using some completely idiosyncratic definition of "emergent" here.

> I expect this has as much to do with our lack of precision in our tools rather than with the concept in principle.

Unfortunately in this case it is the exact opposite.

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>you must be using some completely idiosyncratic definition of "emergent" here.

I doubt my usage is idiosyncratic. Rather, the term is so ill defined that people often use it inappropriately. Emergent means something along the lines of not easily predictable behavior from the behavior of the component parts. My claim is nothing in LLMs is emergent in the sense of being inexplicable from an ideal consideration of simple/linear aggregations of its neurons. This isn't something I can prove, but it certainly isn't obviously false from just considering recent mathematical results. Higher order/non-linear interactions are characteristic of this kind of inexplicable behavior from complex interactions we characterize as emergent. But there are good reasons to think this kind of non-linearity in any semantic regime is next to impossible in principle.

Semantic geometric representations are characterized by linearity in response dynamics such that a linear change in one variable results in a linear semantic change in related variables. That's how vector embeddings capture semantic relationships. Call it the linearity hypothesis. Non-linear dynamics in the embedding space just destroys semantic relationships in most cases. There is no way to keep an entire network of relationships sufficiently correlated if non-linear response dynamics is a part of the representational toolbox. Non-linear dynamics in general kills correlations.

Testing this against recent LLM proofs, what would need to be shown is that the relevant neural activations do not decompose the semantic content of the proofs (or the reasoning trace) in a straightforward manner, but rather the reasoning trace is unpredictable from the semantic content of the activated neurons that precede the tokens of the reasoning trace. But this idea strains credulity; it is almost inconceivable that this would be the case. We should be highly incredulous of the idea that complex non-linear interactions can be wholly responsible for the long-running coherent output in LLMs. The default hypothesis should be that there is no emergent behavior (as I defined it) in neural networks.

> Emergent means something along the lines of not easily predictable behavior from the behavior of the component parts.

Yes, partly. But it also refers to complexity arising from the component parts that requires additional tools (formulas, descriptions, etc) to meaningfully describe. Or at least, emergentism has always formally involved more than mere predictability from parts (https://plato.stanford.edu/entries/properties-emergent/). I am talking about weak emergentism here, so no need to get into metaphysics and mysticism.

> My claim is nothing in LLMs is emergent in the sense of being inexplicable from an ideal consideration of simple/linear aggregations of its neurons.

IMO this contradicts even your minimalist definition. Almost no one predicted simple LLMs would be where they were today, and you can't currently predict their behavior by looking at their weights.

Something doesn't cease to become emergent if you can roughly explain it by making references to its parts. We can broadly explain how human behaviours result from drives + sensory processing + associations and etc., but human beings are not very predictable in many cases, and also exhibit consistent behaviors that tend to be very hard to predict (or even explain) from basic knowledge of the brain, drives, and body. LLMs are no different, and what they are doing today couldn't be more clearly emergent, even by your definition.

Also, something doesn't cease to be emergent even if you can perfectly explain it with reference to the parts. E.g. attractors are emergent behaviour, even though they fall out of very simple formulas and so in a sense are completely due to their defining parts. But describing the shapes of e.g. the Mandelbrot set or the Lorentz Attractor requires more than the base formula that generated them. So they are classic examples of emergent phenomena. Likewise, a person's intelligence might one day be fully predictable from biological and genetic knowledge and measurements, but it wouldn't mean that intelligence stops being an emergent property that is still observable and useful to use itself in other models.

You can for example argue that an LLM has no emergent drives (https://xxcancel.com/jon_stokes/status/2111158233993797870#m), and that would be a coherent claim, under a certain definition of "drive".

But your argument here I cannot follow clearly, because LLM behavior is clearly emergent in any sense of the term, including yours. EDIT: Or basically, LLMs show weak emergence, but if you disagree with this, then it would seem to me you perhaps disagree with the concept of emergence at all.

>IMO this contradicts even your minimalist definition. Almost no one predicted simple LLMs would be where they were today, and you can't currently predict their behavior by looking at their weights.

Whether some trait is emergent shouldn't depend on the state of our knowledge. That no one predicted how far LLMs would go is a demerit for our model of them and doesn't imply they have extremely complex "emergent" interactions. My conception of emergence is asking whether something can be "easily" predicted from an ideal understanding of the component parts. Simple/linear aggregations of the correct semantics of precursor neurons is what is at stake here. In other words, is understanding the semantics of the precursor neurons the vast majority of the hard part in understanding what comes next? Past failures of prediction do not bear on this question much at all.

An example from the article that is informative to my view is the association the network made to the base64 encoding of plain text substrings. If you had asked me, I would have assumed the network decodes base64 live as it traverses the layers. Instead (at least some of) these associations are precomputed during pretraining and recorded in the form of shared affinity in neurons. Active computation and precomputed associations are on two ends of the intelligence spectrum. The human brain is strongly biased towards precomputed associations. It seems that LLMs are as well. The more I see about their inner workings, the more I suspect they operate mostly in the form of these explicit associations. The computations they actively manifest across the network can't be that complicated due to depth constraints. There just aren't enough compute steps[1] for the kinds of complex, non-linear, unpredictable dynamics you get from compute heavy regimes.

>Something doesn't cease to become emergent if you can roughly explain it by making references to its parts. We can broadly explain how human behaviours result from drives + sensory processing + associations and etc.

When I talk about explaining some phenomena, I mean it in a robust sense that e.g. gives you the capacity to predict downstream behavior with high accuracy.

[1] These discussions always seem to have difficulty distinguish the computation that realizes the system from the computation the system as such engages in. There is a huge amount of compute that realizes the feed forward pass of an LLM. But the computational steps directed by the LLM to resolve its output from the prompt is severely restricted by the layers in the forward pass of the network (or however many virtual layers from looping).

The way I interpret emergent is 'not something that was obvious from the properties of the parts', so you could study the parts in isolation and never realize that there was this much more complex behavior that would only emerge if you placed those objects together beyond a certain scale.

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