Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models
A technical manual documents an open toolkit for measuring contextual individuation in transformer language models using bridge forms—single written words that recur unchanged across subject domains with different senses. The pipeline includes declarative specification of bridge forms, corpus acquisition from Wikipedia, occurrence localization, layer-wise representation extraction, domain-pairwise silhouette measurement, and paired visualization. Design choices address sense contamination, multi-group bias of silhouette coefficient, subword-tokenization misalignment, and axis-comparability artifacts.
The evidence is a technical manual for an open toolkit that measures whether transformer language models individuate word occurrences by context. It introduces bridge forms: a single written word that appears unchanged across two or more subject domains with a different sense in each. The toolkit pipeline includes specifying bridge forms and source domains, acquiring Wikipedia corpus, localizing occurrences, extracting layer-wise representations, measuring domain-pairwise silhouette separation, and visualizing results. The manual justifies design choices to avoid methodological failure modes such as sense contamination from broad category labels, multi-group bias of the silhouette coefficient, subword-tokenization misalignment, and axis-comparability artifacts.
The toolkit operationalizes contextual individuation by holding the word form fixed while varying context and sense, enabling controlled measurement of representation separation across domains. It uses domain-pairwise silhouette scores to quantify separation in the model's representation space, avoiding multi-group bias. The pipeline addresses subword-tokenization misalignment, ensuring that representations correspond to the intended word occurrence. The paired visualization protocol likely enables comparison of representation geometry across layers or domains.
This toolkit provides a standardized method for evaluating a specific aspect of language model interpretability: how context shapes word representations. It could be adopted by researchers and practitioners to audit models for sense disambiguation capabilities. The open-source nature may encourage community contributions and benchmarking. The focus on Wikipedia as a corpus suggests broad applicability to general-domain models.
The toolkit offers value to AI research teams and model developers by providing a reproducible method to assess contextual representation quality. It could inform model selection or fine-tuning for tasks requiring word sense disambiguation. As an open resource, it may reduce development time for interpretability analyses and support model auditing for robustness.
Potential next signals include publication of empirical results using the toolkit on popular transformer models, adoption in interpretability benchmarks, or extension to other languages and domains. The toolkit may be used to compare models' contextual individuation across architectures or training regimes. Further methodological refinements could address additional failure modes or incorporate other separation metrics.