LLM翻译成功不依赖语义理解，仅靠语言分布模式即可。
Translation Indeterminacy and the Distributional Fallacy
- 用生态具身认知视角，认为意义来自人与环境互动
- 翻译可仅凭跨语言分布对应关系完成，无需理解语义
- 适合研究AI翻译机制或哲学认知的读者
大型语言模型（LLMs）常基于分布假设：（1）语义源于语言上下文的分布模式；（2）掌握跨语言分布对应关系即可实现有效翻译。本文否定第一点，认为语言分布是意义生成实践的结果而非源头；接受第二点，主张翻译（人类或机器）的成功不依赖语义或指称，只需掌握跨语言分布对应及其推理组织即可。论文提出生态具身论视角，认为参考和意义根植于主体-环境互动，通过行动支撑的概念稳定形成，而当前的LLMs缺乏此类世界参与的认知能力。
原文摘要 · Abstract (English)
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.
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