# Unmasking the Factual-Conceptual Gap in Persian Language Models

arXiv: https://arxiv.org/abs/2602.17623 · HTML: https://mlsanity.com/paper/2602.17623/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 19 Feb 2026.

## Builds on (most cited first, 19 of 19)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024)
- [Prompting GPT-3 To Be Reliable](https://mlsanity.com/paper/2210.09150/) (2022)
- [Experience Grounds Language](https://mlsanity.com/paper/2004.10151/) (2020)
- [Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning](https://mlsanity.com/paper/2305.14160/) (2023)
- [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022)
- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
- [From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning](https://mlsanity.com/paper/2310.00492/) (2023)
- [ParsiNLU: A Suite of Language Understanding Challenges for Persian](https://mlsanity.com/paper/2012.06154/) (2020)
- [FarsTail: A Persian Natural Language Inference Dataset](https://mlsanity.com/paper/2009.08820/) (2020)
- [Khayyam Challenge (PersianMMLU): Is Your LLM Truly Wise to The Persian Language?](https://mlsanity.com/paper/2404.06644/) (2024)
- [CultureScope: A Dimensional Lens for Probing Cultural Understanding in LLMs](https://mlsanity.com/paper/2509.16188/) (2025)
- [PerCul: A Story-Driven Cultural Evaluation of LLMs in Persian](https://mlsanity.com/paper/2502.07459/) (2025)
- [Nunchi-Bench: Benchmarking Language Models on Cultural Reasoning with a Focus on Korean Superstition](https://mlsanity.com/paper/2507.04014/) (2025)
- [We Politely Insist: Your LLM Must Learn the Persian Art of Taarof](https://mlsanity.com/paper/2509.01035/) (2025)
- [MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment](https://mlsanity.com/paper/2508.17290/) (2025)
- [CRaFT: An Explanation-Based Framework for Evaluating Cultural Reasoning in Multilingual Language Models](https://mlsanity.com/paper/2510.14014/) (2025)

## Built on by (most cited first, 1 of 1)

- [PersLitEval: Fine-grained Benchmark and Evaluation of LLMs on Persian Literature Questions](https://mlsanity.com/paper/2605.27015/) (2026)

## Abstract (arXiv)

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.