Differences in Typological Alignment in Language Models' Treatment of Differential Argument Marking

Iskar Deng, Nathalia Xu, Shane Steinert-Threlkeld

Keywords: differential argument marking, typology, learnability, controlled corpus experiments

TL;DR: We train language models on synthetic differential argument marking corpora and find selective alignment with typological tendencies.

Abstract: Recent work has shown that language models (LMs) trained on synthetic corpora can exhibit typological preferences that resemble cross-linguistic regularities in human languages, particularly for syntactic phenomena such as word order. In this paper, we extend this paradigm to differential argument marking (DAM), a semantic licensing system in which morphological marking depends on semantic prominence. Using a controlled synthetic learning method, we train GPT-2 models on 18 corpora implementing distinct DAM systems and evaluate their generalization using minimal pairs. Our results reveal a dissociation between two typological dimensions of DAM. Models reliably exhibit human-like preferences for natural markedness direction, favoring systems in which overt marking targets semantically atypical arguments. In contrast, models do not reproduce the strong object preference in human languages, in which overt marking in DAM more often targets objects rather than subjects. These findings suggest that different typological tendencies may arise from distinct underlying sources.

Scope Confirmation: To the best of my judgment, this submission falls within the scope of CoNLL.

Primary Area Selection: Typology and Multilinguality

Secondary Area Selection: Computational Psycholinguistics, Cognition and Linguistics

Use Of Generative Artificial Intelligence Tools: Yes, for writing code

Data Collection From Human Subjects: No

Submission Type: Archival: I certify that the submission has not been previously published, nor is the material in it under review by another journal or conference. Further, no material in it will be submitted for review at another conference or journal while under review by CoNLL 2026.

Submission Number: 89