Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models

Abstract: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's 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's 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.

29 pages, 2 figures (one with 2 subfigures), 1 table. Toolkit, source code, and corpora archived separately on Zenodo (see Section 9)

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

I.2.7; I.2.6; I.5.3

arXiv:2609.05333 [cs.AI]

(or arXiv:2609.05333v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.05333 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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