Think Before You Link: Rarity, Reasoning, and Retrieval in Multilingual Entity Linking

Key points

To complement view-count-based rarity measures, the authors built an evaluation set of rare entities using structural signals such as Wikidata link counts and description metadata.
A reasoning-capable vision-language model iteratively searches English Wikipedia to gather evidence; no additional model training was done for the proposed procedure.
On the MERLIN benchmark spanning Hindi, Indonesian, Japanese, Tamil, and Vietnamese, combining retrieval with reasoning performed best, while reasoning alone did not significantly improve rare-entity accuracy.
Adding retrieval alone can improve rare-entity performance while hurting overall accuracy, showing that reasoning over retrieved evidence matters in multilingual entity linking.
Applicability is limited to entities with an English Wikipedia article, and search failures dominate the residual errors, leaving retrieval quality as the main open problem.

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