Transformer 语言模型在其嵌入层, 为单词类型分配一个与上下文无关的, 向量，但人们普遍认为可以通过后面层中的上下文来区分该单词的 的出现。干净地测试这个信念需要一个结构，该结构保持单词形式固定，而其上下文和预期含义以受控的,标记方式变化。本手册记录了围绕这样一个构造, 构建的开放工具包，我们将其称为桥梁形式: 一个单个书面单词，在两个或多个主题域中重复, 不变,，每个主题域具有不同的含义。我们描述,并证明,管道的每个阶段:桥形式及其源域的声明性规范,从维基百科获取语料库,出现定位,逐层表示提取,模型中分离的域成对轮廓测量的表示空间,和配对可视化协议。每个设计选择都与方法故障模式一起呈现，旨在避免(来自过于广泛的类别标签的意义污染,轮廓系数的多组偏差,子字标记化错位,以及降维图中的轴可比伪影,等等)。本手稿是方法论和实施参考:，它不报告或解释在任何特定模型或桥形式集上运行工具包的经验结果。工具包, 的完整源代码, 以及用于执行该工具包的语料库在持久标识符, 下单独存档(第9) 部分，并旨在被使用它来生成和解释经验结果的研究引用为工具。

A transformer language model assigns a single, context-independent vector to a word type at its embedding layer, yet is widely believed to individuate that word的 occurrences by context in its later layers. Testing this belief cleanly requires a construct that holds the word form fixed while its context and intended sense vary in a controlled, labeled way. This manual documents an open toolkit built around such a construct, which we call a bridge form: a single written word that recurs, unchanged, across two or more subject domains with a different sense in each. We describe, and justify, every stage of the pipeline: the declarative specification of bridge forms and their source domains, corpus acquisition from Wikipedia, occurrence localization, layer-wise representation extraction, a domain-pairwise silhouette measurement of separation in the model的 representation space, and a paired visualization protocol. Each design choice is presented together with the methodological failure mode it is meant to avoid (sense contamination from overly broad category labels, the multi-group bias of the silhouette coefficient, subword-tokenization misalignment, and axis-comparability artifacts in dimensionality-reduced plots, among others). This manuscript is a methodological and implementation reference: it does not report or interpret empirical outcomes of running the toolkit on any particular model or bridge-form set. The toolkit, its full source, and the corpora used to exercise it are archived separately (Section 9) under a persistent identifier, and are intended to be cited as an instrument by studies that use it to produce and interpret empirical results.

科目: 人工智能 (cs.AI); 计算和语言 (cs.CL)

Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)