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.
668cf1b27f96b83037411b4a
Amy Xin
claimed_verified
2024-07-09T08:17:42.259Z
660bf98c3336a7e128a0e918
/avatars/3e3f2886bd4a730ec19b13aecc99279f.svg
Amy Xin
user
amyxx2001
668cf1b27f96b83037411b4b
Yunjia Qi
admin_assigned
2024-07-09T08:38:07.751Z
6556cf2cee35f7d8bcf13bb3
/avatars/0a945054cb4732c2ee7a5502c42628bf.svg
Qi Yunjia
user
Kikkk
668cf1b27f96b83037411b4c
Zijun Yao
claimed_verified
2024-07-09T08:17:43.791Z
648c4b46e549be47af1aafcd
https://cdn-avatars.huggingface.co/v1/production/uploads/648c4b46e549be47af1aafcd/YgOHbmUM2EDM-lb7GdqXz.jpeg
Zijun
user
TranSirius
668cf1b27f96b83037411b4d
Fangwei Zhu
admin_assigned
2024-07-09T08:38:13.246Z
654cca3fe1b4cd6d40d5a7ae
/avatars/4d09531277e16ad71474fc888c16b227.svg
Fangwei Zhu
user
soliz1998
668cf1b27f96b83037411b4e
Kaisheng Zeng
668cf1b27f96b83037411b4f
Xu Bin
668cf1b27f96b83037411b50
Lei Hou
668cf1b27f96b83037411b51
Juanzi Li
admin_assigned
2024-07-09T08:39:00.208Z
65df8cbc2705d9672f55d1aa
/avatars/63e46f15bb76bd9d4508fd0f54f39829.svg
Juanzi Li
user
juanli
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.
LLM-Augmented Entity Linking (LLMAEL) improves entity linking performance by augmenting traditional models with mention-centered descriptions generated by large language models.
entity linking long-tail entities large language models LLM LLM-Augmented Entity Linking LLMAEL mention-centered descriptions fine-tuned model state-of-the-art results