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

Amy Xin, Qi Yunjia, Zijun, Fangwei Zhu, Kaisheng Zeng, Xu Bin, Lei Hou, Juanzi Li

LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking: 3 upvotes on Hugging Face Daily Papers, #16 of 17 papers on 2024-07-09. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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