Translation Indeterminacy and the Distributional Fallacy

2026-09-07

AI Summary

本文探讨了大语言模型的翻译不确定性问题，通过生态-互动主义视角解释意义和指称源于主体与环境的互动，并认为跨语言分布对应关系足以支持翻译。

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.

Problem

• large language models
• distributional hypothesis
• semantic meaning
• translation

Innovation

• ecological-enactivist perspective
• distributional hypothesis
• translation indeterminacy
• interlingual distributional correspondence