From Jam to Language Models, from Food Law to Zeji-LM / 从果酱到语言模型, 从食品法到择继语言模型

Starting from a jar of jam that is “about to go public,” this essay examines the epistemology behind definitions of language models: how we come to know an object, why we select certain properties as explanations, and where we conflate mechanisms of formation, category identity, performance evaluation, and institutional recognition. American disputes over jam regulation do not provide legal backing that proves Zeji-LM correct; rather, they raise a set of independently examinable questions: What does a name constrain? On what grounds does a standard stand? How does a producer's declaration become a verifiable judgment? This essay argues that computational mechanisms can provide rigorous and valid mathematical definitions, but cross-disciplinary explanation and public discussion must also state the object being modeled, the scope of application, and the boundaries of the evidence. Zeji-LM's significance lies first in a procedural proposal: to give training selection an ex ante basis and traceable records, and to test separately what was transmitted and what the model actually retained. The essay does not claim that selecting training data necessarily reduces hallucinations, nor does it infer model consciousness from anthropomorphic output. Humor opens the questions; evidence decides which statements may remain. 本文从一瓶“准备上市的果酱”出发,讨论语言模型定义背后的认识论:我们通过什么认识一个对象,为什么选择某些性质作为解释,又在何处把形成机制、类别身份、表现评价与制度认可混成了一件事。美国果酱监管争议提供的,不是证明 Zeji-LM 正确的法律背书,而是一组可独立审查的问题:名称约束什么,标准凭什么成立,生产者的声明如何变成可核验的判断。本文主张,计算机制能够给出严格而有效的数学定义,但在跨领域解释与公共讨论中,还须说明其建模对象、适用范围与证据边界。Zeji-LM 的意义首先是一项程序性提议:让训练选择具有事前依据和可追溯记录,并将传递了什么与模型实际保留了什么分开检验。本文不主张选择语料必然减少幻觉,也不由拟人化输出推断模型意识。幽默负责打开问题,证据负责决定哪些话能够留下。