Farsi Natural Language Processing: A Survey

Variants of Persian (Farsi, Dari, and Tajiki) are spoken by more than 110 million people worldwide. Despite the growing interest in broadening the coverage of Natural Language Processing (NLP) methods, Persian has received limited attention. This survey addresses this gap by reviewing over 200 peer-reviewed studies published across the last two decades, documenting more than 40 publicly available corpora, and analysing linguistic challenges, pipelines, and applications across approaches ranging from rule-based methods to Large Language Models (LLMs). Given the rapid advancement of LLMs, we study the state of Persian-specific models across different tasks, review the available LLMs for Persian, assess their performance and limitations, and provide a comprehensive discussion of their strengths and gaps. Our analysis shows that although attention to Persian NLP has increased and notable progress has been made, critical gaps remain—particularly the scarcity of standardised benchmarks and gold corpora in tasks such as toxicity and safety, bias and fairness, and explainability and reasoning. Future progress requires expanding culturally grounded datasets, establishing robust evaluation frameworks, and fostering open-source collaboration and shared-task participation to accelerate the development of Persian NLP.