mlsanityData to 7 Oct 2026

NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data

arXiv 2402.15343 ↗First posted 23 Feb 2024cs.CL cs.AI cs.LGcited by 34

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Abstract (arXiv)

Large Language Models (LLMs) have shown impressive abilities in data annotation, opening the way for new approaches to solve classic NLP problems. In this paper, we show how to use LLMs to create NuNER, a compact language representation model specialized in the Named Entity Recognition (NER) task. NuNER can be fine-tuned to solve downstream NER problems in a data-efficient way, outperforming similar-sized foundation models in the few-shot regime and competing with much larger LLMs. We find that the size and entity-type diversity of the pre-training dataset are key to achieving good performance. We view NuNER as a member of the broader family of task-specific foundation models, recently unlocked by LLMs.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 26 of its 37 references that are in the map. First posted on arXiv 23 Feb 2024.

Builds on

26 of 37 references.

References · 26

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14 more
  1. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,404
  2. 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 849
  3. 2023InstructUIE: Multi-task Instruction Tuning for Unified Information Extractioncited by 167
  4. 2023GPT-NER: Named Entity Recognition via Large Language Modelscited by 158
  5. 2021Few-NERD: A Few-Shot Named Entity Recognition Datasetcited by 150
  6. 2023GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extractioncited by 116
  7. 2023GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformercited by 84
  8. 2023GPTEval: A Survey on Assessments of ChatGPT and GPT-4cited by 73
  9. 2023UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognitioncited by 70
  10. 2019Low-Resource Name Tagging Learned with Weakly Labeled Datacited by 32
  11. 2020Coarse-to-Fine Pre-training for Named Entity Recognitioncited by 28
  12. 2022Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learningcited by 24
  13. 2021NER-BERT: A Pre-trained Model for Low-Resource Entity Taggingcited by 21
  14. 2023Type-Aware Decomposed Framework for Few-Shot Named Entity Recognitioncited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 12; 2026: 16.

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  1. 2025GLiREL -- Generalist Model for Zero-Shot Relation Extractioncited by 11
  2. 2025Overview of BioASQ 2025: The Thirteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answeringcited by 11
  3. 2024ReflectDiffu:Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Frameworkcited by 6
  4. 2025GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interfacecited by 6
  5. 2024HistoLens: An LLM-Powered Framework for Multi-Layered Analysis of Historical Texts -- A Case Application of Yantie Luncited by 5
  6. 2024WhisperNER: Unified Open Named Entity and Speech Recognitioncited by 3
  7. 2025GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognitioncited by 3
  8. 2024Automatic Classification of News Subjects in Broadcast News: Application to a Gender Bias Representation Analysiscited by 2
  9. 2025FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognitioncited by 2
  10. 2024SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularizationcited by 1
  11. 2024Retrieval-Enhanced Named Entity Recognitioncited by 1
  12. 2025KG2QA: Knowledge Graph-enhanced Retrieval-augmented Generation for Communication Standards Question Answeringcited by 1
22 more
  1. 2025SpeechLLM: Unified Speech and Language Model for Enhanced Multi-Task Understanding in Low Resource Settingscited by 1
  2. 2026GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extractioncited by 1
  3. 2026LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptationcited by 1
  4. 2025Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?cited by 0
  5. 2025Arce: Augmented Roberta with Contextualized Elucidations for Ner in Automated Rule Checkingcited by 0
  6. 2025A Tale of LLMs and Induced Small Proxies: Scalable Small Language Models for Knowledge Miningcited by 0
  7. 2025Named Entity Recognition in COVID-19 tweets with Entity Knowledge Augmentationcited by 0
  8. 2025Multi-Task Pre-Finetuning of Lightweight Transformer Encoders for Text Classification and NERcited by 0
  9. 2026What Matters When Building Universal Multilingual Named Entity Recognition Models?cited by 0
  10. 2026A Semi-Automated Annotation Workflow for Paediatric Histopathology Reports Using Small Language Modelscited by 0
  11. 2026Just Pass Twice: Efficient Token Classification with LLMs for Zero-Shot NERcited by 0
  12. 2026Universal NER v2: Towards a Massively Multilingual Named Entity Recognition Benchmarkcited by 0
  13. 2026DiZiNER: Disagreement-guided Instruction Refinement via Pilot Annotation Simulation for Zero-shot Named Entity Recognitioncited by 0
  14. 2026Decoding Text Spans for Efficient and Accurate Named-Entity Recognitioncited by 0
  15. 2026MultiSoc-4D: A Benchmark for Diagnosing Instruction-Induced Label Collapse in Closed-Set LLM Annotation of Bengali Social Mediacited by 0
  16. 2026Opir: Efficient Multi-Task Safety Classification for Toxicity, Jailbreaks, Hate Speech, and Harmful Contentcited by 0
  17. 2026Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognitioncited by 0
  18. 2026Task Decomposition for Efficient Annotationcited by 0
  19. 2026Error-Type-Aware Loss Reweighting for Robust Named Entity Recognition with Noisy LLM Labelscited by 0
  20. 2026NE-R1: Enhancing Named Entity Recognition Model via Reinforcement Learningcited by 0
  21. 2026Overview of BioASQ 2026: The fourteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answeringcited by 0
  22. 2026On-Device Named-Entity Recognition: A Deployability Study of Accuracy, Cost, Reliability, and Confidencecited by 0