Evaluating Large Language Models on Named Entity Recognition
Summary
This research evaluates 28 large language models on named entity recognition (NER, the task of identifying and labeling people, places, and organizations in text) across 13 datasets to understand how well they perform. The study found that all models experience hallucinations (where the AI generates false or unsupported information), but a two-phase framework called LLM-NER that includes a "Check phase" to verify recognized entities can help reduce these errors.
Solution / Mitigation
The source proposes an LLM-NER framework with a Check phase designed to mitigate hallucinations: "the Check guides LLMs to examine the correctness of recognized entities, which is designed to mitigate hallucinations in the NER scenario." The research demonstrates this approach is "a feasible way to alleviate hallucinations."
Classification
Original source: http://ieeexplore.ieee.org/document/11474527
First tracked: June 28, 2026 at 02:03 AM
Classified by LLM (prompt v3) · confidence: 92%