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.

翻译：大型语言模型（LLMs，如ChatGPT）在命名实体识别（NER）中展现出了卓越的零样本和少样本能力。然而，这些模型仅能通过在线API访问，可能导致数据泄露和不可复现问题。本文提出VicunaNER——一种基于新发布的开源LLM——Vicuna的零/少样本NER框架。VicunaNER是一个两阶段框架，每个阶段利用与Vicuna的多轮对话从文本中识别实体。我们将第二阶段称为重识别（Re-Recognition），用于识别第一阶段（即识别阶段）未识别出的实体。此外，我们在每个阶段设置实体正确性检查对话，以过滤错误实体。我们在涵盖5个领域的10个数据集上评估VicunaNER的零样本能力，并在Few-NERD上评估其少样本能力。实验结果表明，VicunaNER在两种设置下均取得了优越性能。此外，我们从多个角度对Vicuna进行了全面研究。