Unmasking the Factual-Conceptual Gap in Persian Language Models
This work addresses the issue of cultural competence in Persian NLP, highlighting a critical failure in current models for researchers and developers in low-resource language AI.
The paper tackled the problem of Persian language models confusing memorized cultural facts with reasoning about implicit social norms, revealing through DivanBench that models show a 21% performance gap between factual retrieval and scenario application and exhibit severe acquiescence bias.
While emerging Persian NLP benchmarks have expanded into pragmatics and politeness, they rarely distinguish between memorized cultural facts and the ability to reason about implicit social norms. We introduce DivanBench, a diagnostic benchmark focused on superstitions and customs, arbitrary, context-dependent rules that resist simple logical deduction. Through 315 questions across three task types (factual retrieval, paired scenario verification, and situational reasoning), we evaluate seven Persian LLMs and reveal three critical failures: most models exhibit severe acquiescence bias, correctly identifying appropriate behaviors but failing to reject clear violations; continuous Persian pretraining amplifies this bias rather than improving reasoning, often degrading the model's ability to discern contradictions; and all models show a 21\% performance gap between retrieving factual knowledge and applying it in scenarios. These findings demonstrate that cultural competence requires more than scaling monolingual data, as current models learn to mimic cultural patterns without internalizing the underlying schemas.