A Data-free Universal Prior over Syntactic Structures
Abstract
The probabilities of syntactic structures in human languages are assumed to emerge fully from language-specific experience. Here, I show that a universal prior over syntactic structures emerges from a model of human language production, in which words are progressively integrated into syntactic structure. Without fitting any parameters to specific language data, the resulting prior assigns higher probabilities to attested than to random dependency trees in all 138 typologically diverse languages examined. These prior probabilities correlate positively with those estimated from corpora in 33 of 34 languages. The results indicate that part of the probability structure of syntax can arise independently of language-specific learning. This identifies human language production as a possible cognitive source of universal statistical structure in language, while providing a data-independent structural bias for probabilistic models, including large language models.
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BibTeX
@misc{martin2026datafree,
title = {{A Data-free Universal Prior over Syntactic Structures}},
author = {Fermín Moscoso del Prado Martín},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.16854},
eprint = {2609.16854},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.16854},
}
RIS
TY - UNPB
TI - A Data-free Universal Prior over Syntactic Structures
AU - Fermín Moscoso del Prado Martín
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2609.16854
UR - https://arxiv.org/abs/2609.16854
AB - The probabilities of syntactic structures in human languages are assumed to emerge fully from language-specific experience. Here, I show that a universal prior over syntactic structures emerges from a model of human language production, in which words are progressively integrated into syntactic structure. Without fitting any parameters to specific language data, the resulting prior assigns higher probabilities to attested than to random dependency trees in all 138 typologically diverse languages examined. These prior probabilities correlate positively with those estimated from corpora in 33 of 34 languages. The results indicate that part of the probability structure of syntax can arise independently of language-specific learning. This identifies human language production as a possible cognitive source of universal statistical structure in language, while providing a data-independent structural bias for probabilistic models, including large language models.
ER -