title: LLMAEL: Large Language Models are Good Context Augmenters for Entity
    Linking
summary: 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_authors._id: 668cf1b27f96b83037411b4a
paper_authors.name: Amy Xin
paper_authors.status: claimed_verified
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paper_authors.fullname: Amy Xin
paper_authors.type: user
paper_authors.user: amyxx2001
paper_authors._id: 668cf1b27f96b83037411b4b
paper_authors.name: Yunjia Qi
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paper_authors.fullname: Qi Yunjia
paper_authors.type: user
paper_authors.user: Kikkk
paper_authors._id: 668cf1b27f96b83037411b4c
paper_authors.name: Zijun Yao
paper_authors.status: claimed_verified
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paper_authors.fullname: Zijun
paper_authors.type: user
paper_authors.user: TranSirius
paper_authors._id: 668cf1b27f96b83037411b4d
paper_authors.name: Fangwei Zhu
paper_authors.status: admin_assigned
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paper_authors._id: 654cca3fe1b4cd6d40d5a7ae
paper_authors.avatarUrl: /avatars/4d09531277e16ad71474fc888c16b227.svg
paper_authors.fullname: Fangwei Zhu
paper_authors.type: user
paper_authors.user: soliz1998
paper_authors._id: 668cf1b27f96b83037411b4e
paper_authors.name: Kaisheng Zeng
paper_authors._id: 668cf1b27f96b83037411b4f
paper_authors.name: Xu Bin
paper_authors._id: 668cf1b27f96b83037411b50
paper_authors.name: Lei Hou
paper_authors._id: 668cf1b27f96b83037411b51
paper_authors.name: Juanzi Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-09T08:39:00.208Z
paper_authors._id: 65df8cbc2705d9672f55d1aa
paper_authors.avatarUrl: /avatars/63e46f15bb76bd9d4508fd0f54f39829.svg
paper_authors.fullname: Juanzi Li
paper_authors.type: user
paper_authors.user: juanli
paper_title: LLMAEL: Large Language Models are Good Context Augmenters for Entity
    Linking
paper_summary: 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_ai_keywords: Entity Linking (EL) large language models (LLMs) light disambiguation long-tail entities parameter-efficient fine-tuning plug-and-play approach knowledgeable context augmenters mention-centered advantages dataset benchmarks