@inproceedings{gupta-etal-2025-hats,
    title = "{HATS} : {H}indi Analogy Test Set for Evaluating Reasoning in Large Language Models",
    author = "Gupta, Ashray  and
      Joseph, Rohan  and
      Rai, Sunny",
    editor = "Rambelli, Giulia  and
      Ilievski, Filip  and
      Bolognesi, Marianna  and
      Sommerauer, Pia",
    booktitle = "Proceedings of the 2nd Workshop on Analogical Abstraction in Cognition, Perception, and Language (Analogy-Angle II)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.analogyangle-1.6/",
    doi = "10.18653/v1/2025.analogyangle-1.6",
    pages = "57--80",
    ISBN = "979-8-89176-274-9",
    abstract = "Analogies test a model{'}s ability to infer implicit relationships between concepts, making them a key benchmark for evaluating reasoning capabilities. While large language models (LLMs) are widely evaluated for reasoning in English, their abilities in Indic languages remain understudied, limiting our understanding of whether these models generalize across languages. To address this gap, we introduce a new Hindi Analogy Test Set (HATS), comprising 405 multiple-choice questions sourced from Indian government exams. We benchmark state-of-the-art multilingual LLMs using various prompting strategies and introduce a grounded Chain of Thought approach that leverages cognitive theories of analogical reasoning. This approach improves model performance on Hindi analogy questions. Our experiments show that models perform best with English prompts, irrespective of the prompting strategy. Our test set addresses the lack of a critical resource to evaluate LLM reasoning capabilities in Hindi. The test set is publicly available for research purposes here https://github.com/Inequilazitive/HATS-Hindi{\_}Analogy{\_}Test{\_}Set"
}