All Entities are Not Created Equal: Examining the Long Tail for Ultra-Fine Entity Typing

Abstract:Due to their capacity to acquire world knowledge from large corpora, pre-trained language models (PLMs) are extensively used in ultra-fine entity typing tasks where the space of labels is extremely large. In this work, we explore the limitations of the knowledge acquired by PLMs by proposing a novel heuristic to approximate the pre-training distribution of entities when the pre-training data is unknown. Then, we systematically demonstrate that entity-typing approaches that rely solely on the parametric knowledge of PLMs struggle significantly with entities at the long tail of the pre-training distribution, and that knowledge-infused approaches can account for some of these shortcomings. Our findings suggest that we need to go beyond PLMs to produce solutions that perform well for infrequent entities.

Computation and Language (cs.CL)

arXiv:2410.17355 [cs.CL]

(or arXiv:2410.17355v4 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2410.17355 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.18653/v1/2025.starsem-1.15 Focus to learn more DOI(s) linking to related resources

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