CaLMQA: Exploring culturally specific long-form question answering across 23 languages

• URL: http://arxiv.org/abs/2406.17761v3
• Date: Wed, 11 Jun 2025 16:56:58 GMT
• Title: CaLMQA: Exploring culturally specific long-form question answering across 23 languages
• Authors: Shane Arora, Marzena Karpinska, Hung-Ting Chen, Ipsita Bhattacharjee, Mohit Iyyer, Eunsol Choi,
• Abstract summary: CaLMQA is a dataset of 51.7K culturally specific questions across 23 different languages.<n>We evaluate factuality, relevance and surface-level quality of LLM-generated long-form answers.
• Score: 58.18984409715615
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite rising global usage of large language models (LLMs), their ability to generate long-form answers to culturally specific questions remains unexplored in many languages. To fill this gap, we perform the first study of textual multilingual long-form QA by creating CaLMQA, a dataset of 51.7K culturally specific questions across 23 different languages. We define culturally specific questions as those that refer to concepts unique to one or a few cultures, or have different answers depending on the cultural or regional context. We obtain these questions by crawling naturally-occurring questions from community web forums in high-resource languages, and by hiring native speakers to write questions in under-resourced, rarely-studied languages such as Fijian and Kirundi. Our data collection methodologies are translation-free, enabling the collection of culturally unique questions like "Kuber iki umwami wa mbere w'uburundi yitwa Ntare?" (Kirundi; English translation: "Why was the first king of Burundi called Ntare (Lion)?"). We evaluate factuality, relevance and surface-level quality of LLM-generated long-form answers, finding that (1) for many languages, even the best models make critical surface-level errors (e.g., answering in the wrong language, repetition), especially for low-resource languages; and (2) answers to culturally specific questions contain more factual errors than answers to culturally agnostic questions -- questions that have consistent meaning and answer across many cultures. We release CaLMQA to facilitate future research in cultural and multilingual long-form QA.

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