TailNLG: A Multilingual Benchmark Addressing Verbalization of Long-Tail Entities

• URL: http://arxiv.org/abs/2603.27768v1
• Date: Sun, 29 Mar 2026 17:01:54 GMT
• Title: TailNLG: A Multilingual Benchmark Addressing Verbalization of Long-Tail Entities
• Abstract summary: We present the first systematic study of long-tail entities in Data-to-Text generation.<n>We introduce TailNLG, a new multilingual benchmark in English, Italian, and Spanish, built from Wikidata.<n>We show that the impact of long-tail entities varies across models and languages, and that existing evaluation metrics do not consistently capture these differences.
• Score: 35.1254326443212
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The automatic verbalization of structured knowledge is a key task for making knowledge graphs accessible to non-expert users and supporting retrieval-augmented generation systems. Although recent advances in Data-to-Text generation have improved multilingual coverage, little attention has been paid to potential biases in the verbalization of rare entities, frequently known as long-tail entities. In this work, we present the first systematic study of long-tail entities in Data-to-Text generation. We introduce TailNLG, a new multilingual benchmark in English, Italian, and Spanish, built from Wikidata and covering entities with varying levels of popularity. We evaluate three different families of large language models in zero-shot settings and compare their performance on rare versus common entities, as well as against the established WebNLG benchmark. Our results reveal a consistent bias against long-tail entities: embedding-based scores are lower, and model uncertainty is higher for rare entities. We further show that the impact of long-tail entities varies across models and languages, and that existing evaluation metrics do not consistently capture these differences, highlighting the need for more reliable evaluation frameworks.

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