VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning
  with Large Language Models
Seoyeon Kim Kwangwook Seo Hyungjoo Chae Jinyoung Yeo Dongha Lee
Recent approaches in domain-specific named entity recognition (NER), such as
biomedical NER, have shown remarkable advances. However, they still lack of
faithfulness, producing erroneous predictions. We assume that knowledge of
entities can be useful in verifying the correctness of the predictions. Despite
the usefulness of knowledge, resolving such errors with knowledge is
nontrivial, since the knowledge itself does not directly indicate the
ground-truth label. To this end, we propose VerifiNER, a post-hoc verification
framework that identifies errors from existing NER methods using knowledge and
revises them into more faithful predictions. Our framework leverages the
reasoning abilities of large language models to adequately ground on knowledge
and the contextual information in the verification process. We validate
effectiveness of VerifiNER through extensive experiments on biomedical
datasets. The results suggest that VerifiNER can successfully verify errors
from existing models as a model-agnostic approach. Further analyses on
out-of-domain and low-resource settings show the usefulness of VerifiNER on
real-world applications.