The growing use of Devanagari-script languages such as Hindi, Nepali, Marathi, Sanskrit, and Bhojpuri on social media presents unique challenges for natural language understanding (NLU), particularly in language identification, hate speech detection, and target classification. To address these challenges, we organized a shared task with three subtasks: (i) identifying the language of Devanagari-script text, (ii) detecting hate speech, and (iii) classifying hate speech targets into individual, community, or organization. A curated dataset combining multiple corpora was provided, with splits for training, evaluation, and testing. The task attracted 113 participants, with 32 teams submitting models evaluated on accuracy, precision, recall, and macro F1-score. Participants applied innovative methods, including large language models, transformer models, and multilingual embeddings, to tackle the linguistic complexities of Devanagari-script languages. This paper summarizes the shared task, datasets, and results, and aims to contribute to advancing NLU for low-resource languages and fostering inclusive, culturally aware natural language processing (NLP) solutions.

Understanding and interpreting culturally specific language remains a significant challenge for multilingual natural language processing (NLP) systems, particularly for less-resourced languages. To address this problem, this paper introduces PRONE, a novel dataset of 2,830 Nepali proverbs, and evaluates the performance of various language models (LMs) in two tasks: (i) identifying the correct meaning of a proverb from multiple choices, and (ii) categorizing proverbs into predefined thematic categories. The models, including both open-source and proprietary, were tested in zero-shot and few-shot settings with prompts in English and Nepali. While models like GPT-4o demonstrated promising results and achieved the highest performance among LMs, they still fall short of human-level accuracy in understanding and categorizing culturally nuanced content, highlighting the need for more inclusive NLP.