. . . . "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." . "2026-09-29T12:00:00+02:00"^^ . "Sala Comuni" . "2026-09-29T09:00:00+02:00"^^ . . . . "X-TAIL: Extraction and exploitation of long-TAIL Knowledge with LLMs and KGs" . . . . . . . . "2026-09-18T07:05:12Z"^^ . . . . "X-TAIL: Extraction and exploitation of long-TAIL Knowledge with LLMs and KGs" . . . . . . "RSA" . "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEA+i9DV8ynzO944pYkYsDwtlqJHuk+b7FQt6Bb4TIQSDX3jGUURn6WpaNwESHmgMLWSydaH5XlabMzuqpPL4iyB+IAzvwIxdJBLhe39EQMu2j+FZ/5d/xu/gO3nZw2KoEvJ7cJisabuI05OylIRSJ+OG5CmzFxnjojUEJeCFi1njY1InhXrb0yHud9b1ifzweoN6YYOBecKs1gTOaT2bf+t4Wgzg3M0PmRVZ4F0OALsnh4T5+x7kFtiPcDKNpORi585JjKCZ4kczIF6oUKcwgC0kmIY97jtLqSLcXRkxHx5CDI6qz4XlRFs1ZbCUdSlzjdPZAkaBcNem1gIhWzkHkEnQIDAQAB" . "FBnEhFZ5sNTY/V2lDVmigAF96fZ/CvJxU07+ITEcMSi5GVO+NJjQ3UzRcqEZTOnjaIVrcEkmR09kYr9ZPNxVc3M8ps4DW/eOVA1+H2a+fw1q8TJzWwbpbvI9jGdBQue+/hevfD7sCbazeiRNROeZtg7pwJddXUd6UGaQJ9Od7lGsjZnpwZMys4Fx8FmAVBkTnQIt0+hcov4luKJNo4Cfc8eNb3ncTxMtqny+3csjxDjV6Dsc2LjjuD5TEUx9nazTfVsyCMTCPQAOMk8cJsfnK7N7vFrF/j9c6z/TKMafsu+E8JqcuXlXvwvva4LGiKUfleawkP/RDNW86c7qr8dSBA==" . . .