This has been my hobby project for some time that combines cultural anthropology, my original field. Most of my research was a combination of cognitive domain analysis and social network analysis. This is a personal project I’ve been working on for a few of months in spare time: What would happen if I used cultural domain analysis methods, free-list, pile sorts, and similar techniques against a large language model? Next token predictors don’t have a culture to model of course, but can such an exercise show anything about the underlying organization of data as the tokens are output? Can it reveal anything about bias? It started as a what-if project to see if current frontier models could even build something like this. The entire process of how LLMs are prompted, collection methods, and how the measures are calculated are documented in the public repo link below. Primarily, I want to see if this is just an idle curiosity in the many intersections of technology and culture I have, or as my father used to say "Is there any water under this bridge?If this is of interest, or you believe it might interest others, please pass it on. Currently, it is a proof of concept and freely available for use/modificaion/forking, etc. The repo and code are 100% public at https://github.com/Mark1999/latent-structure-benchmark “Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.” submitted by /u/No-Reserve2026
Originally posted by u/No-Reserve2026 on r/ArtificialInteligence
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