Clicking through the linked references: The OpenAI "research" reads
like a list of press releases promoting their agenda; Anthropic's list
reads much more like SciAm or similar level of popular publication
trying to actually communicate the issues:
https://transformer-circuits.pub/2022/toy_model/index.html#motivation
My "dive" into this is still very shallow (maybe someone can
de-metaphorize this for me?) but it aligns well with my own ad-hoc/naive
growing apprehensions. Lots here (for me) including what feels like a
strong parallel (apt for metaphorical/analogical domain transfer?) with
genotype/phenotype thinking.
On 7/17/2025 11:20 AM, Steve Smith wrote:
https://scitechdaily.com/researchers-decode-how-we-turn-thoughts-into-sentences/
I'm hoping/expecting some folks here are as fascinated with these
things as I am? LLM's, interperatability, natural vs are to me as
weather/vortices/entropy-intuition is to Nick?
As someone who spends way too much time composing sentences (in
writing) through this impedence-mismatched interface (keyboard) I have
a strong (if misleading, or at least ideosyncratic) apprehension of
how I might form sentences from thoughts, and perhaps even
forward/back propogate possible expressions and structures *all the
way* to where I imagine my interlocutors (often all y'all here)
reading and responding internally (mentally) and online. My
engagement with the LLMs in "casual conversation" includes a great
deal of this, albeit understanding that I'm talking to "a stochastic
parrot" or more aptly perhaps "making faces into a funhouse mirror"
(reminding me that I really want to compose a good-faith answer to
glen's very sincere and I think pivotal questions about metaphor).
I haven't parsed the linked article deeply yet and have not sought out
the actual paper itself yet, but find the ideas presented very
provocative or at least evocative? It triggers hopeful imaginings
about connections with the cortical column work of Hawkins/Numenta as
well as the never ending topics of FriAM: " Effing the inEffabl"e and
"Metaphors all the way Down?"
I don't expect this line of research to *answer* those questions, but
possibly shed some scattered light onto their periphery (oupsie, I
waxed up another metapho to shoot some curls)? For example, might
the electrocorticography during ideation-to-speech transmogrification
show us how strongly metaphorical constructions differ from more
concise or formal analogical versions (if they are a spectrum) or how
attempts to "eff the ineffable" might yield widely branching (bushy)
explorations, ending in some kind of truncation by fatigue or
(de)saturation?
https://www.nature.com/articles/s44271-025-00270-1
<https://www.nature.com/articles/s44271-025-00270-1>
And are attempts at Interpreting LLMs in some meaningful way colinear
or offer important parallax (to reference the
"steam-engine/thermodynamics" duality)?
And me, here, with obviously "way too much time" on my hands and a
fascination with LLMs and an urgency to try to keep traction on the
increasing slope of "the singularity" and a mild facility with visual
analytics and *I* haven't even begun to keep up... This list
(ironically) was formulated by GPT and I've not (and surely will not)
do much double-checking beyond (hopefully) diving deep(er) inoto the
work. I was mildly surprised there were no 2025 references... I'm
guessing the blogs are running commentary including current work.
I'll go click through as soon as I hit <send> here (imagine the
next-token prediction I am doing as I decide to try to stop typing and
hit <send>?)
*“A Survey of Explainability and Interpretability in Large
Language Models”* (ACM Computing Surveys, 2024)
Comprehensive classification of methods, with comparisons between
mechanistic and post‑hoc approaches.
Preprint link on arXiv: [arXiv:2310.01789]
<https://arxiv.org/abs/2310.01789>
*Anthropic’s Interpretability Research Pages* (2023–2024)
https://www.anthropic.com/research
*OpenAI’s Technical Blog: “Language Models and Interpretability”*
(2023)
Discussion of interpretability challenges, with examples from
GPT‑4-level models:
https://openai.com/research
*NeurIPS 2023 Workshop on XAI for Large Models*
Video talks & proceedings with up-to-date methods:
https://nips.cc/virtual/2023/workshop/66533
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