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

arXiv: https://arxiv.org/abs/2406.04528 · HTML: https://mlsanity.com/paper/2406.04528/

## 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 (most cited first, 23 of 23)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping](https://mlsanity.com/paper/2002.06305/) (2020)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models](https://mlsanity.com/paper/2303.10420/) (2023)
- [WARP: Word-level Adversarial ReProgramming](https://mlsanity.com/paper/2101.00121/) (2021)
- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [Few-Shot Text Generation with Pattern-Exploiting Training](https://mlsanity.com/paper/2012.11926/) (2020)
- [Automated Concatenation of Embeddings for Structured Prediction](https://mlsanity.com/paper/2010.05006/) (2020)
- [Empirical Study of Zero-Shot NER with ChatGPT](https://mlsanity.com/paper/2310.10035/) (2023)
- [Guideline Learning for In-context Information Extraction](https://mlsanity.com/paper/2310.05066/) (2023)
- [Identifying and Extracting Rare Disease Phenotypes with Large Language Models](https://mlsanity.com/paper/2306.12656/) (2023)
- [LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text](https://mlsanity.com/paper/2402.04335/) (2024)
- [Rethinking Skill Extraction in the Job Market Domain using Large Language Models](https://mlsanity.com/paper/2402.03832/) (2024)
- [Fine-grained Contract NER using instruction based model](https://mlsanity.com/paper/2401.13545/) (2024)

## Built on by (most cited first, 4 of 4)

- [Towards Refining Developer Questions using LLM-Based Named Entity Recognition for Developer Chatroom Conversations](https://mlsanity.com/paper/2503.00673/) (2025)
- [An LLM + ASP Workflow for Joint Entity-Relation Extraction](https://mlsanity.com/paper/2508.12611/) (2025)
- [Harnessing Large Language Models for Biomedical Named Entity Recognition](https://mlsanity.com/paper/2512.22738/) (2025)
- [KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction](https://mlsanity.com/paper/2602.07303/) (2026)

## 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.