Comment by hackinthebochs

>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).