[FRIAM] Language Model Understanding

[Steve Smith]

This popular-press article came through my Google News feed recently 
which I thought might be useful to the Journalists/English-Majors on the 
list to help understand how LLMs work, etc.   When I read it in detail 
(forwarded from my TS (TinyScreenPhone) on my LS (Large Screen Laptop)) 
I found it a bit more detailed and technical than I'd expected, but 
nevertheless rewarding and possibly offering some traction to 
Journalism/English majors as well as those with a larger investment in 
the CS/Math implied.

    Decomposing Language Models into Understandable Components
    <https://www.anthropic.com/index/decomposing-language-models-into-understandable-components>

and the (more) technical paper behind the article

    https://transformer-circuits.pub/2023/monosemantic-features/index.html
    <https://transformer-circuits.pub/2023/monosemantic-features/index.html>

Despite having sent a few dogs into vaguely similar scuffles in my 
careen(r):

    Faceted Ontologies for Pre Incident Indicator Analysis
    <https://apps.dtic.mil/sti/tr/pdf/ADA588086.pdf>
    SpindleViz <https://www.ehu.eus/ccwintco/uploads/c/c6/HAIS2010_925.pdf>
    ...

... I admit to finding this both intriguing and well over my head on 
casual inspection...  the (metaphorical?) keywords that drew me in  most 
strongly included /Superposition/ and /Thought Vectors/, though they are 
(nod to Glen) probably riddled (heaped, overflowing, bursting, bloated 
... )  with excess meaning.

https://gabgoh.github.io/ThoughtVectors/

This leads me (surprise!) to an open ended discursive series of thoughts 
probably better left for a separate posting (probably rendered in a 
semasiographic language like Heptapod B 
<https://en.wikipedia.org/wiki/Heptapod_languages#Orthography>).

<must... stop... now... >

- Steve
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