The Iranic language family includes many underrepresented languages and dialects that remain largely unexplored in modern NLP research. We introduce APARSIN, a multi-variety benchmark covering 14 Iranic languages, dialects, and accents, designed for sentiment analysis and machine translation. The dataset includes both high and low-resource varieties, several of which are endangered, capturing linguistic variation across them. We evaluate a set of instruction-tuned Large Language Models (LLMs) on these tasks and analyze their performance across the varieties. Our results highlight substantial performance gaps between standard Persian and other Iranic languages and dialects, demonstrating the need for more inclusive multilingual and dialectally diverse NLP benchmarks.

Gender bias in LLMs has been documented across many overt domains, such as colors, emotions, and occupations, while subtler forms rooted in household labor remain understudied. Following Daminger (2019), we distinguish physical household tasks from their cognitive counterparts, which are rarely recognized as labor despite being time-consuming. We adapt the name-generation probing method of Kalhor and Bahrak (2025) to ten household domains, each phrased as both a physical task and its cognitive counterpart, and evaluate six open-source models from three model families in Persian and English. On average, cognitive tasks receive a higher share of feminine names than their physical counterparts in both languages (Wilcoxon signed-rank test); the direction holds in 8 of 10 domains in Persian and 6 of 10 in English. Averaged over models and tasks, skew toward one gender is larger in English than in Persian, in contrast to the overall pattern reported for other domains, but prompts and labeling procedures differ across languages, so we treat this comparison as suggestive. Our results indicate that the cross-linguistic strength of gender bias depends on the domain and model rather than being a fixed property of a language pair.