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    dct:description "LLMs encode world knowledge through pre-training on massive datasets, making them the backbone of knowledge extraction tasks. Their reliability degrades on long-tail knowledge: low-popularity knowledge that occurs infrequently in pre-training data. Popularity is not a neutral property: pre-training datasets are predominantly web-crawled and, as such, are generalist, English-centric, and mostly produced over the past 30 years by Western, High-income, Educated, Liberal, Male-dominated (WHELM, Daryani et al 2025 ) communities, raising the risk of models underperforming on specialised domains, non-English languages and non-contemporary times sources, and on knowledge belonging to marginalised social groups. Retrieval-Augmented Generation has been proposed as a mitigation, but corpora used for retrieval may still be biased. Knowledge Graphs (KGs) provide a more transparent and deterministic alternative, yet open-domain KGs such as Wikidata exhibit coverage gaps along the same dimensions. The X-TAIL workshop aims to advance research on extracting, exploiting, and ultimately preserving long-tail knowledge. It welcomes contributions on: methods to extract knowledge from domain-specific, multilingual, historical, and low-resource language sources, including approaches combining LLMs and KGs; studies on the head/tail knowledge distinction; investigations on how popularity distributes across the specificity, linguistic, temporal, and cultural dimensions of knowledge, and its effect on system performance; characterisation of gaps in knowledge bases and mitigation strategies.";
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