Differences in Typological Alignment in Language Models' Treatment of Differential Argument Marking
arXiv:2602.17653v2 Announce Type: replace 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 generali
Ongoing research into the linguistic capabilities and limitations of large language models is continually pushing the boundaries of AI understanding.
This research provides insights into how AI models learn and replicate complex linguistic structures, which is crucial for developing more human-like and versatile AI.
Our understanding of the typological alignment of language models with human languages is deepened, specifically in the area of differential argument marking.
- · AI researchers
- · NLP developers
- · Linguists
- · Those relying on simplistic AI linguistic models
Improved understanding of language model learning mechanisms and biases.
Development of more robust and culturally/linguistically aware AI applications.
Enhanced cross-cultural communication tools and reduced linguistic barriers in global AI deployment.
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