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

arXiv:2410.17355v4 Announce Type: replace 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.

Related

• Just Pass Twice: Efficient Token Classification with LLMs for Zero-Shot NER
• Strategies for Span Labeling with Large Language Models
• ToMMeR -- Efficient Entity Mention Detection from Large Language Models

Source: arXiv cs.CL | 2026-09-14