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This paper challenges the foundational distributional hypothesis underlying current large language models, arguing that semantic meaning arises from agent-environment interactions rather than statistical patterns alone. While acknowledging that cross-linguistic distributional correspondences can facilitate translation without explicit access to meaning, the authors contend that existing LLMs lack the action-grounded cognition required for true world-involving understanding. This theoretical critique offers a critical perspective on the limitations of purely statistical approaches in achieving robust semantic grounding and reliable machine translation.
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Translation Indeterminacy and the Distributional Fallacy

arXiv2 min read

arXiv:2609.07717v1 Announce Type: new Abstract: 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: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.

Computation and Language (cs.CL)

arXiv:2609.07717 [cs.CL]

(or arXiv:2609.07717v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.07717 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 225 words

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