mlsanityData to 7 Oct 2026

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

arXiv 2305.03253 ↗First posted 5 May 2023cs.CLcited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 5 May 2023.

Builds on

15 of 23 references.

References · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  5. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
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  7. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
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  9. 2023ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPTcited by 250
  10. 2021Few-NERD: A Few-Shot Named Entity Recognition Datasetcited by 150
  11. 2022Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Againcited by 83
  12. 2021CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learningcited by 64
3 more
  1. 2023ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extractioncited by 38
  2. 2021An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labelingcited by 26
  3. 2022Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypescited by 17

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 0; 2026: 3.

9 papers.

  1. 2023Iterative Zero-Shot LLM Prompting for Knowledge Graph Constructioncited by 25
  2. 2024Can LLMs be Good Graph Judge for Knowledge Graph Construction?cited by 11
  3. 2024Leveraging Graph Structures to Detect Hallucinations in Large Language Modelscited by 7
  4. 2024P-ICL: Point In-Context Learning for Named Entity Recognition with Large Language Modelscited by 5
  5. 2026A Statistical Framework for Auditing Behavioral Dependence and Induced Bias in LLM Judgescited by 4
  6. 2024Deep Learning Based Named Entity Recognition Models for Recipescited by 3
  7. 2024Utilizing Large Language Models for Named Entity Recognition in Traditional Chinese Medicine against COVID-19 Literature: Comparative Studycited by 1
  8. 2026Translation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource Languagescited by 0
  9. 2026Mitigating Entity Type Confusion in Cross-Domain NER via Multidimensional Quantification and Reasoning Enhancementcited by 0