LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking

• URL: http://arxiv.org/abs/2407.04020v2
• Date: Mon, 15 Jul 2024 12:47:39 GMT
• Title: LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking
• Authors: Amy Xin, Yunjia Qi, Zijun Yao, Fangwei Zhu, Kaisheng Zeng, Xu Bin, Lei Hou, Juanzi Li,
• Abstract summary: Large language models (LLMs) are more robust at interpreting uncommon mentions. We introduce LLM-Augmented Entity Linking LLMAEL, a plug-and-play approach to enhance entity linking. Experiments on 6 standard datasets show that the vanilla LLMAEL outperforms baseline EL models in most cases.
• Score: 35.393279375085854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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