[FRIAM] Dissecting Recall of Factual Associations in, Auto-Regressive Language Models

[Steve Smith]

https://arxiv.org/pdf/2304.14767.pdf

I am pretty much over my head in this literature, but continue to be 
fascinated as I watch people who are not try to untangle some 
explanatory power in their models...

The details of this analysis or framing this as /information flow/ 
rather than /static data/structure/ is reminiscent of some very nascent 
work we *tried* to do 15 years ago, attempting to analyze/understand 
huge Systems Dynamics models of Critical Infrastructure joined 
together/coupled to try to predict the potential for cascading failures 
through these coupled systems.   The representation *as* SD models were 
natural for this framing but we made only the tiniest progress IMO in 
extracting hints of *explanatory* narratives.    I was primarily doing 
visualization on those tasks but tried to focus on clustering of the 
Dual Graph/Network  to find structure in the *flow* during extreme 
events rather than in the engineered/designed structure of the network 
itself.

I know there are others on this list who have worked with complex, 
dynamic networks  (I'm thinking of Frank's colleagues and Causal 
Discovery in Graphical Models,   various project Glen has alluded to, 
and a wide variety of problems Stephen has related to me over the years, 
but I'm sure there are plenty of others)... I'm curious if anyone else 
is wading in this deep (and more to the point, finding any traction)?

 From the paper:
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