# GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer

arXiv: https://arxiv.org/abs/2311.08526 · HTML: https://mlsanity.com/paper/2311.08526/

## Key facts

- Cited by 88 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 70 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 30 of 36)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://mlsanity.com/paper/2301.13688/) (2023)
- [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://mlsanity.com/paper/2104.08773/) (2021)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models](https://mlsanity.com/paper/2303.10420/) (2023)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning](https://mlsanity.com/paper/2304.05613/) (2023)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning](https://mlsanity.com/paper/2305.13971/) (2023)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction](https://mlsanity.com/paper/2310.03668/) (2023)
- [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2012.04373/) (2020)
- [Scalable Zero-shot Entity Linking with Dense Entity Retrieval](https://mlsanity.com/paper/1911.03814/) (2019)
- [SpanNER: Named Entity Re-/Recognition as Span Prediction](https://mlsanity.com/paper/2106.00641/) (2021)

## Built on by (most cited first, 30 of 88)

- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [SciReplicate-Bench: Benchmarking LLMs in Agent-driven Algorithmic Reproduction from Research Papers](https://mlsanity.com/paper/2504.00255/) (2025)
- [NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data](https://mlsanity.com/paper/2402.15343/) (2024)
- [3D CAVLA: Leveraging Depth and 3D Context to Generalize Vision Language Action Models for Unseen Tasks](https://mlsanity.com/paper/2505.05800/) (2025)
- [GLiClass: Generalist Lightweight Model for Sequence Classification Tasks](https://mlsanity.com/paper/2508.07662/) (2025)
- [MedCodER: A Generative AI Assistant for Medical Coding](https://mlsanity.com/paper/2409.15368/) (2024)
- [Overview of BioASQ 2025: The Thirteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering](https://mlsanity.com/paper/2508.20554/) (2025)
- [Towards More Realistic Extraction Attacks: An Adversarial Perspective](https://mlsanity.com/paper/2407.02596/) (2024)
- [HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language Models](https://mlsanity.com/paper/2409.10419/) (2024)
- [PaDeLLM-NER: Parallel Decoding in Large Language Models for Named Entity Recognition](https://mlsanity.com/paper/2402.04838/) (2024)
- [Anonymization-Enhanced Privacy Protection for Mobile GUI Agents: Available but Invisible](https://mlsanity.com/paper/2602.10139/) (2026)
- [Show Less, Instruct More: Enriching Prompts with Definitions and Guidelines for Zero-Shot NER](https://mlsanity.com/paper/2407.01272/) (2024)
- [Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities](https://mlsanity.com/paper/2510.07037/) (2025)
- [The Million-Label NER: Breaking Scale Barriers with GLiNER bi-encoder](https://mlsanity.com/paper/2602.18487/) (2026)
- [Curating Grounded Synthetic Data with Global Perspectives for Equitable AI](https://mlsanity.com/paper/2406.10258/) (2024)
- [VGGHeads: 3D Multi Head Alignment with a Large-Scale Synthetic Dataset](https://mlsanity.com/paper/2407.18245/) (2024)
- [ANCHOLIK-NER: A Benchmark Dataset for Bangla Regional Named Entity Recognition](https://mlsanity.com/paper/2502.11198/) (2025)
- [CUS-QA: Local-Knowledge-Oriented Open-Ended Question Answering Dataset](https://mlsanity.com/paper/2507.22752/) (2025)
- [WhisperNER: Unified Open Named Entity and Speech Recognition](https://mlsanity.com/paper/2409.08107/) (2024)
- [GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition](https://mlsanity.com/paper/2504.00676/) (2025)
- [Locate-and-Focus: Enhancing Terminology Translation in Speech Language Models](https://mlsanity.com/paper/2507.18263/) (2025)
- [Typed Decision Models: An Early Evidence Audit and Evaluation Checklist](https://mlsanity.com/paper/2609.32160/) (2026)
- [LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice](https://mlsanity.com/paper/2501.10915/) (2025)
- [Named Entity Recognition in Historical Italian: The Case of Giacomo Leopardi's Zibaldone](https://mlsanity.com/paper/2505.20113/) (2025)
- [Pioneer Agent: Continual Improvement of Small Language Models in Production](https://mlsanity.com/paper/2604.09791/) (2026)
- [GLiNER Guard: Unified Encoder Family for Production LLM Safety and Privacy](https://mlsanity.com/paper/2605.05277/) (2026)
- [GLiNER2-PII: A Multilingual Model for Personally Identifiable Information Extraction](https://mlsanity.com/paper/2605.09973/) (2026)
- [NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition](https://mlsanity.com/paper/2405.07609/) (2024)
- [Retrieval-Enhanced Named Entity Recognition](https://mlsanity.com/paper/2410.13118/) (2024)
- [Extracting Information in a Low-resource Setting: Case Study on Bioinformatics Workflows](https://mlsanity.com/paper/2411.19295/) (2024)

## Abstract (arXiv)

Named Entity Recognition (NER) is essential in various Natural Language Processing (NLP) applications. Traditional NER models are effective but limited to a set of predefined entity types. In contrast, Large Language Models (LLMs) can extract arbitrary entities through natural language instructions, offering greater flexibility. However, their size and cost, particularly for those accessed via APIs like ChatGPT, make them impractical in resource-limited scenarios. In this paper, we introduce a compact NER model trained to identify any type of entity. Leveraging a bidirectional transformer encoder, our model, GLiNER, facilitates parallel entity extraction, an advantage over the slow sequential token generation of LLMs. Through comprehensive testing, GLiNER demonstrate strong performance, outperforming both ChatGPT and fine-tuned LLMs in zero-shot evaluations on various NER benchmarks.