Multilingual and Cross-Lingual Citation Needed Detection on Wikipedia for Lower-Resource Languages
Year: 2026
Type: conference-paper
Abstract: In automated fact-checking (AFC), checkworthiness detection identifies claims requiring verification based on domain-specific criteria.On Wikipedia, this task instantiates as Citation Needed Detection (CND), which flags claims lacking supporting citations.However, existing research has largely overlooked lowerresource languages, and recent AFC pipelines rely on large language models (LLMs), which are inaccessible to low-resource organizations.We introduce MCN, a multilingual CND corpus spanning 18 languages across three resource levels, on which we conduct an extensive study of small decoder-based language models (SLMs).Our experiments show that SLMs fine-tuned with an encoder-style objective substantially outperform prompted LLMs across languages.We further present one of the first studies on cross-lingual CND, demonstrating that SLMs fine-tuned solely on English claims surpass LLMs, even with little to no target-language adaptation.Our findings have important implications for lower-resource Wikipedia communities and suggest that compact, task-specific models are preferable to LLMs for CND.We release all data and code at https://github.com/gerritq/mcnDataset Task Domain Languages (resource-level) Size NLP4IF 2021 (Shaar et al., 2021) CD/CWD Covid-19/Politics 3 (2 high, 1 medium) 1.3K-4K CitationNeeded (Redi et al., 2019) CWD Wikipedia 3 (3 high) 20K Kazemi et al. (2021) CD Covid-19, Politics 5 (2 high, 2 medium, 1 low) 5K Dutta et al. (2023) CD Politics 3 (2 high, 1 medium) 600-1.4KHalitaj and Zubiaga (2024) CWD Wikipedia 3 (2 high, 1 medium) 31K-1.1mCheckThat 2024 (Hasanain et al., 2024) CD/CWD Politics, Covid-19 3 (3 high) 1K-23K Baigutanova et al. (2026) CWD Wikipedia 5 (5 high) 20K MCN (ours) CWD Wikipedia 18 (8 high, 5 medium, 5 low) 2K-1.1m
Institutions: King's College London, Wikimedia Foundation
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166883853