Multilingual and Cross-Lingual Citation Needed Detection on Wikipedia for Lower-Resource Languages

• URL: http://arxiv.org/abs/2605.31136v1
• Date: Fri, 29 May 2026 10:47:22 GMT
• Title: Multilingual and Cross-Lingual Citation Needed Detection on Wikipedia for Lower-Resource Languages
• Abstract summary: We introduce MCN, a multilingual CND corpus spanning 18 languages across three resource levels.<n>We conduct an extensive study of small decoder-based language models (SLMs)<n>Experiments show that SLMs fine-tuned with an encoder-style objective substantially outperform prompted LLMs across languages.
• Score: 4.596698321408836
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In automated fact-checking (AFC), check-worthiness 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 lower-resource 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/mcn

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