mlsanityData to 14 Oct 2026

llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models

arXiv 2406.04528 ↗First posted 6 Jun 2024cs.CLcited by 4

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Abstract (arXiv)

Large language models (LLMs) allow us to generate high-quality human-like text. One interesting task in natural language processing (NLP) is named entity recognition (NER), which seeks to detect mentions of relevant information in documents. This paper presents llmNER, a Python library for implementing zero-shot and few-shot NER with LLMs; by providing an easy-to-use interface, llmNER can compose prompts, query the model, and parse the completion returned by the LLM. Also, the library enables the user to perform prompt engineering efficiently by providing a simple interface to test multiple variables. We validated our software on two NER tasks to show the library's flexibility. llmNER aims to push the boundaries of in-context learning research by removing the barrier of the prompting and parsing steps.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 23 of its 39 references that are in the map. First posted on arXiv 6 Jun 2024.

Builds on

23 of 39 references.

References · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2020Scaling Laws for Neural Language Modelscited by 6,586
  3. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  4. 2023A Survey of Large Language Modelscited by 2,576
  5. 2022Emergent Abilities of Large Language Modelscited by 2,512
  6. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  7. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  8. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
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  10. 2020Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stoppingcited by 384
  11. 2019Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approachcited by 308
  12. 2023A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Modelscited by 246
11 more
  1. 2021WARP: Word-level Adversarial ReProgrammingcited by 221
  2. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 163
  3. 2021Template-Based Named Entity Recognition Using BARTcited by 153
  4. 2020Few-Shot Text Generation with Pattern-Exploiting Trainingcited by 87
  5. 2020Automated Concatenation of Embeddings for Structured Predictioncited by 86
  6. 2023Empirical Study of Zero-Shot NER with ChatGPTcited by 60
  7. 2023Guideline Learning for In-context Information Extractioncited by 24
  8. 2023Identifying and Extracting Rare Disease Phenotypes with Large Language Modelscited by 18
  9. 2024LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Textcited by 12
  10. 2024Rethinking Skill Extraction in the Job Market Domain using Large Language Modelscited by 11
  11. 2024Fine-grained Contract NER using instruction based modelcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Towards Refining Developer Questions using LLM-Based Named Entity Recognition for Developer Chatroom Conversationscited by 0
  2. 2025An LLM + ASP Workflow for Joint Entity-Relation Extractioncited by 0
  3. 2025Harnessing Large Language Models for Biomedical Named Entity Recognitioncited by 0
  4. 2026KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstractioncited by 0