@inproceedings{al-sefat-2025-bengalifig,
    title = "{B}engali{F}ig: A Low-Resource Challenge for Figurative and Culturally Grounded Reasoning in {B}engali",
    author = "Al Sefat, Abdullah",
    editor = "Shukla, Ankita  and
      Kumar, Sandeep  and
      Bedi, Amrit Singh  and
      Chakraborty, Tanmoy",
    booktitle = "Proceedings of the 1st Workshop on Multimodal Models for Low-Resource Contexts and Social Impact (MMLoSo 2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.mmloso-1.6/",
    pages = "58--77",
    ISBN = "979-8-89176-311-1",
    abstract = "Large language models excel on broad multilingual benchmarks but remains to be evaluated extensively in figurative and culturally grounded reasoning, especially in low-resource context. We present **BengaliFig**, a compact yet richly annotated challenge set that targets this gap in Bengali, a widely spoken low-resourced language. The dataset contains 435 unique riddles drawn from Bengali oral and literary traditions. Each item is annotated along five orthogonal dimensions capturing reasoning type, trap type, cultural depth, answer category, and difficulty, and is automatically converted to multiple{--}choice format through a constraint{--}aware, AI{--}assisted pipeline. We evaluate eight frontier LLMs from major providers under zero{--}shot and few{--}shot chain{--}of{--}thought prompting revealing consistent weaknesses in metaphorical and culturally specific reasoning. BengaliFig thus contributes both a diagnostic probe for evaluating LLM robustness in low-resource cultural contexts and a step toward inclusive and heritage-aware NLP evaluation. Data and evaluation code is available at https://github.com/chaoSefat/Bengali-Fig"
}