CaLMQA: Exploring culturally specific long-form question answering across 23 languages
Published in The 63rd Annual Meeting of the Association for Computational Linguistics, 2024
This paper introduces CaLMQA, a benchmark for evaluating long-form question answering systems on culturally specific questions across 23 languages. We analyze how different models handle cultural nuances and language-specific knowledge.
Recommended citation: Shane Arora*, Marzena Karpinska*, Hung-Ting Chen, Ipsita Bhattacharjee, Mohit Iyyer, Eunsol Choi. (2024). "CaLMQA: Exploring culturally specific long-form question answering across 23 languages." The 63rd Annual Meeting of the Association for Computational Linguistics.
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