Large Language Models (LLMs, e.g., ChatGPT) have shown impressive zero- and few-shot capabilities in Named Entity Recognition (NER). However, these models can only be accessed via online APIs, which may cause data leak and non-reproducible problems. In this paper, we propose VicunaNER, a zero/few-shot NER framework based on the newly released open-source LLM -- Vicuna. VicunaNER is a two-phase framework, where each phase leverages multi-turn dialogues with Vicuna to recognize entities from texts. We name the second phase as Re-Recognition, which recognizes those entities not recognized in the first phase (a.k.a. Recognition). Moreover, we set entity correctness check dialogues in each phase to filter out wrong entities. We evaluate VicunaNER's zero-shot capacity on 10 datasets crossing 5 domains and few-shot capacity on Few-NERD. Experimental results demonstrate that VicunaNER achieves superior performance in both shot settings. Additionally, we conduct comprehensive investigations on Vicuna from multiple perspectives.

本文提出了一个基于新近发布的开源Large Language Models模型【Vicuna】的zero/few-shot命名实体识别框架【VicunaNER】。该框架分为两个阶段，每个阶段都利用multi-turn dialogues与模型交互，识别文本中的实体，第二阶段（Re-Recognition）用于识别第一阶段中未被识别的实体。我们在5个领域跨越10个数据集上评估了VicunaNER的表现，实验结果表明该框架在zero/few-shot设置下都表现优异。此外，我们还从多个角度对Vicuna进行了全面的研究。

VicunaNER: 使用Vicuna的零/少样本命名实体识别