# QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition

arXiv: https://arxiv.org/abs/2203.01543 · HTML: https://mlsanity.com/paper/2203.01543/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 25 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020)
- [UnifiedQA: Crossing Format Boundaries With a Single QA System](https://mlsanity.com/paper/2005.00700/) (2020)
- [Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping](https://mlsanity.com/paper/2002.06305/) (2020)
- [True Few-Shot Learning with Language Models](https://mlsanity.com/paper/2105.11447/) (2021)
- [Revisiting Few-sample BERT Fine-tuning](https://mlsanity.com/paper/2006.05987/) (2020)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [Ultra-Fine Entity Typing](https://mlsanity.com/paper/1807.04905/) (2018)
- [Few-Shot Named Entity Recognition: A Comprehensive Study](https://mlsanity.com/paper/2012.14978/) (2020)
- [Accelerating Neural Transformer via an Average Attention Network](https://mlsanity.com/paper/1805.00631/) (2018)
- [Example-Based Named Entity Recognition](https://mlsanity.com/paper/2008.10570/) (2020)

## Built on by (most cited first, 15 of 15)

- [Large Language Models are Few-Shot Clinical Information Extractors](https://mlsanity.com/paper/2205.12689/) (2022)
- [Prompt Engineering for Healthcare: Methodologies and Applications](https://mlsanity.com/paper/2304.14670/) (2023)
- [PromptNER: Prompt Locating and Typing for Named Entity Recognition](https://mlsanity.com/paper/2305.17104/) (2023)
- [How far is Language Model from 100% Few-shot Named Entity Recognition in Medical Domain](https://mlsanity.com/paper/2307.00186/) (2023)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [CoPL: Contextual Prompt Learning for Vision-Language Understanding](https://mlsanity.com/paper/2307.00910/) (2023)
- [Learning In-context Learning for Named Entity Recognition](https://mlsanity.com/paper/2305.11038/) (2023)
- [Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0](https://mlsanity.com/paper/2204.05211/) (2022)
- [GAMedX: Generative AI-based Medical Entity Data Extractor Using Large Language Models](https://mlsanity.com/paper/2405.20585/) (2024)
- [ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER](https://mlsanity.com/paper/2305.17951/) (2023)
- [Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling](https://mlsanity.com/paper/2212.02036/) (2022)
- [From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contexts](https://mlsanity.com/paper/2305.04928/) (2023)
- [Question-Answering System Extracts Information on Injection Drug Use from Clinical Notes](https://mlsanity.com/paper/2305.08777/) (2023)
- [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)
- [Automatic Generation of Highlights for Academic Paper Via Prompt-based Learning](https://mlsanity.com/paper/2606.25253/) (2026)

## Abstract (arXiv)

Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label efficiency. However, previous prompt-based methods for few-shot NER have limitations such as a higher computational complexity, poor zero-shot ability, requiring manual prompt engineering, or lack of prompt robustness. In this work, we address these shortcomings by proposing a new prompt-based learning NER method with Question Answering (QA), called QaNER. Our approach includes 1) a refined strategy for converting NER problems into the QA formulation; 2) NER prompt generation for QA models; 3) prompt-based tuning with QA models on a few annotated NER examples; 4) zero-shot NER by prompting the QA model. Comparing the proposed approach with previous methods, QaNER is faster at inference, insensitive to the prompt quality, and robust to hyper-parameters, as well as demonstrating significantly better low-resource performance and zero-shot capability.