Differences in Typological Alignment in Language Models' Treatment of Differential Argument Marking
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Computer Science > Computation and Language
Title:Differences in Typological Alignment in Language Models' Treatment of Differential Argument Marking
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.
| Comments: | 16 pages, 8 figures, 7 tables. To appear at CoNLL 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2602.17653 [cs.CL] |
| (or arXiv:2602.17653v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2602.17653
arXiv-issued DOI via DataCite
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Submission history
From: Iskar Deng [view email][v1] Thu, 19 Feb 2026 18:56:34 UTC (282 KB)
[v2] Thu, 21 May 2026 21:16:25 UTC (180 KB)
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