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This paper challenges the prevailing view of Large Language Models as mere symbol manipulators, arguing instead that they capture "Cultural Intelligence" by grounding symbols in sociological practices. By reframing model behavior through a lens of distributed representation and social practice, the work offers a significant theoretical shift in how we interpret LLM comprehension beyond standard statistical correlation.
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What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Communication, Vocalisation, and Language

arXiv1 min read

arXiv:2610.16490v1 Announce Type: new Abstract: The contention is that the current batch of Large Language Models don't *understand* language, but they *do* it. Rather than LLMs forming some kind of ``distributed representation'' of symbols, the claim is that LLMs are capturing ``Cultural Intelligence'' and grounding symbols in what the sociologists call practices.

Abstract:The contention is that the current batch of Large Language Models don't *understand* language, but they *do* it. Rather than LLMs forming some kind of ``distributed representation'' of symbols, the claim is that LLMs are capturing ``Cultural Intelligence'' and grounding symbols in what the sociologists call practices.

A written version of my argument with with Roger, with input from Bruce. 6 pages and one (Venn) diagram

Computation and Language (cs.CL)

arXiv:2610.16490 [cs.CL]

(or arXiv:2610.16490v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.16490 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 161 words

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