A Multi-Agent LLM Framework for Multi-Domain Low-Resource In-Context NER via Knowledge Retrieval, Disambiguation and Reflective Analysis

Wenxuan Mu; Jinzhong Ning; Di Zhao; Yijia Zhang

AAAI 2026technical0 citations

A Multi-Agent LLM Framework for Multi-Domain Low-Resource In-Context NER via Knowledge Retrieval, Disambiguation and Reflective Analysis

Wenxuan Mu, Jinzhong Ning, Di Zhao, Yijia Zhang

Abstract

In-context learning (ICL) with large language models (LLMs) has emerged as a promising paradigm for named entity recognition (NER) in low-resource scenarios. However, existing ICL-based NER methods suffer from three key limitations: (1) reliance on dynamic retrieval of annotated examples, which is problematic when annotated data is scarce; (2) limited generalization to unseen domains due to the LLM

BibTeX

@inproceedings{aaai2026_amultiagentllmfr,
  title = {A Multi-Agent LLM Framework for Multi-Domain Low-Resource In-Context NER via Knowledge Retrieval, Disambiguation and Reflective Analysis},
  author = {Wenxuan Mu and Jinzhong Ning and Di Zhao and Yijia Zhang},
  booktitle = {AAAI 2026},
  year = {2026}
}