Communication, Vocalisation, and Language

Peter Wallis

cs.CLOct 12, 2026

Communication, Vocalisation, and Language

Authors: Peter Wallis

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.

Figures & tables

Figure 1: A Venn diagram of language, vocalisation, and communication with human communication in the middle.

LLM Grounding

Large language model (LLM) grounding studies how models anchor answers and actions in evidence, structured knowledge, and real-world context.

See all 319 papers

Translation Indeterminacy and the Distributional Fallacy
Sep 7, 2026Michael CarlLLM Grounding Distributional Semantics
Kent State University Modern and Classical Language Studies
Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives
Apr 28, 2026Yu Wang, Emmanuele Chersoni, Chu-Ren HuangMultilingual Language Model Evaluation LLM Grounding
Department of Language Science and Technology · The Hong Kong Polytechnic University · Hung Lok Road, Hung Hom, Kowloon, Hong Kong (China)
Metaphors We Compute By: A Computational Audit of Cultural Translation vs. Thinking in LLMs
Apr 6, 2026Yuan Chang, Jiaming Qu, Zhu LiCultural Bias in Language Models Cultural Knowledge in Language Models
Meta, Burlingame, CA · Amazon, Seattle, WA

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Translation Indeterminacy and the Distributional Fallacy

Michael Carl

Kent State University Modern and Classical Language Studies

LLM Grounding Distributional Semantics Machine Translation Embodied Cognition

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Large language models (LLMs) are commonly associated with the distributional hypothesis, according to which (1) semantic meaning is grounded in distributional patterns of linguistic context, and (2) knowledge of cross-linguistic distributional correspondences allows for successful translation. This paper rejects the first claim as a causal inversion: linguistic distributions reflect patterns arising from meaning-making practices rather than constituting their source. At the same time, it accepts the second claim, arguing that translation -human or machine - can succeed without requiring access to meaning or reference. Knowledge of interlingual distributional correspondence and their inferential organization may be sufficient for translation. The paper develops an ecological-enactivist perspective, according to which reference and meaning are grounded in agent-environment interaction and stabilized through action-grounded concepts, forms of world-involving cognition that current LLMs do not possess.

Abstract

LLM Grounding

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