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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3ABHJQ4Y9I" target="_blank" >RIV/00216208:11320/26:BHJQ4Y9I - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2025.acl-long.578/" target="_blank" >https://aclanthology.org/2025.acl-long.578/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2025.acl-long.578" target="_blank" >10.18653/v1/2025.acl-long.578</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

ISBN

979-8-89176-251-0

ISSN

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e-ISSN

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Number of pages

46

Pages from-to

11772-11817

Publisher name

Association for Computational Linguistics

Place of publication

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Event location

Vienna, Austria

Event date

Jan 1, 2026

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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