A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition

Abstract

Zero-shot named entity recognition (NER) aims to develop entity recognition systems from unannotated text corpora. This task presents substantial challenges due to minimal human intervention. Recent work has adapted large language models (LLMs) for zero-shot NER by crafting specialized prompt templates. And it advances models' self-learning abilities by incorporating self-annotated demonstrations. Two important challenges persist: (i) Correlations between contexts surrounding entities are overlooked, leading to wrong type predictions or entity omissions. (ii) The indiscriminate use of task demonstrations, retrieved through shallow similarity-based strategies, severely misleads LLMs during inference.

Authors 6

1. Zihan Wang

   University of Amsterdam

   Affiliation as printed

   University of Amsterdam, Amsterdam, Netherlands

2. Ziqi Zhao

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

3. Yougang Lyu

   University of Amsterdam

   Affiliation as printed

   University of Amsterdam, Amsterdam, Netherlands

4. Zhumin Chen

   Shandong University

   Affiliation as printed

   Shandong University, Jinan, China

5. Maarten de Rijke

   University of Amsterdam

   Affiliation as printed

   University of Amsterdam, Amsterdam, Netherlands

6. Zhaochun Ren Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands