Large language models can translate without understanding meaning
Translation Indeterminacy and the Distributional Fallacy
Computation and Language
Summary
This paper looks at how big AI language models translate between languages. It challenges the idea that these models understand meaning by just looking at word patterns. Instead, the authors say that meaning comes from how people interact with the world, which AI does not do. Still, translation can work well because AI learns patterns between languages, not because it understands meaning. The authors suggest that real understanding depends on active engagement with the environment, something current AI models lack.
Large language modelsdistributional hypothesissemantic meaningtranslationcross-linguistic correspondenceecological-enactivist perspectivereferencemeaning-makingagent-environment interactionlanguage understanding
Authors
Michael Carl
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.