Think Before You Link: Rarity, Reasoning, and Retrieval in Multilingual Entity Linking
Multimodal entity linking grounds entity mentions in text and images to knowledge-base entries. These systems degrade on rare entities, but prior work measures rarity primarily through popularity-based metrics such as pageviews. We broaden this view using knowledge-graph structural metrics that capture how well an entity is documented and connected. These metrics identify many rare entities that popularity metrics miss. Across the resulting rare-entity slices, state-of-the-art accuracy drops by 15.4-39.9%, showing that different rarity definitions expose different failure modes. To address these failures, we introduce a simple, training-free framework in which a reasoning-capable vision-l...
| Authors | Parinthapat Pengpun, Simran Khanuja, Graham Neubig |
|---|---|
| Primary category | cs.CL |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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- neulab/merlin-rareDataset · cited by
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- neulab/merlin-rareDataset
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