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

研究论文强调预训练语言模型在处理罕见实体方面的困境

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

一篇题为“并非所有实体都生而平等：探究超细粒度实体类型的长尾效应”的最新研究论文，探讨了预训练语言模型（PLMs）在处理训练数据中出现频率较低的实体时的局限性。该研究提出了一种近似实体预训练分布的方法，并证明了PLMs在处理这些“长尾”实体时存在困难。研究结果表明，当前基于PLM的方法不足以应对需要在罕见实体上实现稳健性能的任务，暗示了对注入知识的方法或替代解决方案的需求。 AI

影响强调了当前语言模型在处理罕见实体方面的局限性，表明在细粒度实体识别方面需要改进方法。

排序理由该集群包含一篇详细介绍预训练语言模型局限性研究结果的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍预训练语言模型局限性研究结果的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Advait Deshmukh, Ashwin Umadi, Dananjay Srinivas, Maria Leonor Pacheco · 2026-09-14 04:00

并非所有实体都生而平等：探究超细粒度实体类型的长尾效应

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 explor…

本文如何被排名

报道来源 [1]

并非所有实体都生而平等：探究超细粒度实体类型的长尾效应

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