VicunaNER: Zero/Few-shot Named Entity Recognition using Vicuna
Bin Ji
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.