# VicunaNER: Zero/Few-shot Named Entity Recognition using Vicuna

arXiv: https://arxiv.org/abs/2305.03253 · HTML: https://mlsanity.com/paper/2305.03253/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [Few-NERD: A Few-Shot Named Entity Recognition Dataset](https://mlsanity.com/paper/2105.07464/) (2021)
- [Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again](https://mlsanity.com/paper/2203.08410/) (2022)
- [CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2109.07589/) (2021)
- [ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction](https://mlsanity.com/paper/2303.05063/) (2023)
- [An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling](https://mlsanity.com/paper/2109.13023/) (2021)
- [Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes](https://mlsanity.com/paper/2208.08023/) (2022)

## Built on by (most cited first, 9 of 9)

- [Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction](https://mlsanity.com/paper/2307.01128/) (2023)
- [Can LLMs be Good Graph Judge for Knowledge Graph Construction?](https://mlsanity.com/paper/2411.17388/) (2024)
- [Leveraging Graph Structures to Detect Hallucinations in Large Language Models](https://mlsanity.com/paper/2407.04485/) (2024)
- [P-ICL: Point In-Context Learning for Named Entity Recognition with Large Language Models](https://mlsanity.com/paper/2405.04960/) (2024)
- [A Statistical Framework for Auditing Behavioral Dependence and Induced Bias in LLM Judges](https://mlsanity.com/paper/2604.07650/) (2026)
- [Deep Learning Based Named Entity Recognition Models for Recipes](https://mlsanity.com/paper/2402.17447/) (2024)
- [Utilizing Large Language Models for Named Entity Recognition in Traditional Chinese Medicine against COVID-19 Literature: Comparative Study](https://mlsanity.com/paper/2408.13501/) (2024)
- [Translation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource Languages](https://mlsanity.com/paper/2607.12612/) (2026)
- [Mitigating Entity Type Confusion in Cross-Domain NER via Multidimensional Quantification and Reasoning Enhancement](https://mlsanity.com/paper/2609.24357/) (2026)

## Abstract (arXiv)

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.