ArticleFrontiers in psychology2026
Relative-to-human benchmark Cognitive Divergence and semantic comprehensibility in Chinese-Uyghur LLM translation.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study examines whether Large Language Models (LLMs) generate Chinese-to-Uyghur translations with syntactic patterns consistent with cognitive efficiency-motivated expectations. We compare translations produced by six mainstream LLMs with a benchmark generated by human experts and used for structural comparison. Syntactic complexity is quantified using Mean Dependency Distance (MDD), and we introduce a relative metric, Cognitive Divergence, as a structural proxy to capture sentence-level deviation from the human benchmark. Semantic comprehensibility is evaluated using COMET scores. The results indicate that LLM-generated texts show no statistically significant difference from the human benchmark in terms of macroscopic syntactic complexity, suggesting a form of surface-level syntactic similarity. However, absolute syntactic complexity alone does not exhibit a reliable association with semantic comprehensibility. In contrast, Cognitive Divergence shows a strong negative association with comprehensibility at the model level (
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