Entity Linking (EL) models are well-trained at mapping mentions to their corresponding entities according to a given context. However, EL models struggle to disambiguate long-tail entities due to their limited training data. Meanwhile, large language models (LLMs) are more robust at interpreting uncommon mentions. Yet, due to a lack of specialized training, LLMs suffer at generating correct entity IDs. Furthermore, training an LLM to perform EL is cost-intensive. Building upon these insights, we introduce LLM-Augmented Entity Linking LLMAEL, a plug-and-play approach to enhance entity linking through LLM data augmentation. We leverage LLMs as knowledgeable context augmenters, generating mention-centered descriptions as additional input, while preserving traditional EL models for task specific processing. Experiments on 6 standard datasets show that the vanilla LLMAEL outperforms baseline EL models in most cases, while the fine-tuned LLMAEL set the new state-of-the-art results across all 6 benchmarks.

翻译：实体链接（EL）模型经过良好训练，能够根据给定上下文将提及映射到其对应的实体。然而，由于训练数据有限，EL模型在消歧长尾实体方面存在困难。与此同时，大型语言模型（LLMs）在解释不常见提及方面表现出更强的鲁棒性。但由于缺乏专门训练，LLMs在生成正确的实体ID方面存在不足。此外，训练LLM执行EL任务成本高昂。基于这些观察，我们提出了LLM增强实体链接（LLMAEL），一种即插即用的方法，通过LLM数据增强来提升实体链接性能。我们利用LLMs作为知识丰富的上下文增强器，生成以提及为中心的描述作为额外输入，同时保留传统EL模型进行任务特定处理。在6个标准数据集上的实验表明，基础版LLMAEL在大多数情况下优于基线EL模型，而经过微调的LLMAEL在所有6个基准测试中均取得了新的最先进结果。