LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking
<|reference_start|>LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking: 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.<|reference_end|>
@article{xin2024llmael:,
    title={LLMAEL: Large Language Models are Good Context Augmenters for Entity
  Linking},
    author={Amy Xin, Yunjia Qi, Zijun Yao, Fangwei Zhu, Kaisheng Zeng, Xu Bin, Lei
  Hou, Juanzi Li},
    journal={arXiv preprint arXiv:2407.04020},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04020},
    primaryClass={cs.CL}
}