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GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer

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arxiv 2311.08526 v1 pith:KWM4TXSZ submitted 2023-11-14 cs.CL cs.AIcs.LG

Urchade Zaratiana , Nadi Tomeh , Pierre Holat , Thierry Charnois This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords entityglinerlanguagellmsmodelbidirectionalchatgptmodels
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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.

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Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extraction

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A fully automated pipeline for generating annotation schemas, guidelines, and synthetic labeled examples from documents improves zero-shot NER after fine-tuning.

  2. GLiREL -- Generalist Model for Zero-Shot Relation Extraction

    cs.CL 2025-01 conditional novelty 6.0 of 10

    A single-pass encoder-scorer model with synthetic LLM pretraining matches or beats prior zero-shot relation classification methods on FewRel and on Wiki-ZSL with 10 or 15 unseen relations, while running far faster.

  3. Nano-ESG: Extracting Corporate Sustainability Information from News Articles

    cs.IR 2024-12 conditional novelty 6.0 of 10

    Nano-ESG is a released dataset of about 51,000 ESG-relevant German corporate news summaries with sentiment, aspect, and timestamp labels, plus an evaluation showing about 80% expert agreement.

  4. Familiarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Data

    cs.CL 2024-12 conditional novelty 6.0 of 10

    Familiarity quantifies semantic overlap between training and evaluation entity types in zero-shot NER, showing that much of the performance gains in current synthetic-data models can be explained by label leakage.

  5. RetrieveAll: A Multilingual Named Entity Recognition Framework with Large Language Models

    cs.CL 2025-05 conditional novelty 5.0 of 10

    RetrieveAll combines per-language LoRA adapters with retrieval of entity and context examples to improve multilingual NER, claiming an average 12.1% F1 gain on PAN-X.

  6. FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual Framework

    cs.CL 2025-02 conditional novelty 5.0 of 10

    FewTopNER reports that adding a topic-modeling branch to a prototype-based few-shot NER model improves multilingual F1 by 2.5 to 4.0 points and increases topic coherence scores.

  7. Enhancing Hindi NER in Low Context: A Comparative study of Transformer-based models with vs. without Retrieval Augmentation

    cs.CL 2025-07 conditional novelty 4.0 of 10

    Adding Wikipedia context to Hindi NER inputs lifts XLM-R from 0.50 to 0.72 macro F1, but helps MuRIL only slightly and hurts or leaves unchanged all Llama-based systems.

  8. LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice

    cs.CL 2025-01 conditional novelty 4.0 of 10

    LegalGuardian masks PII in legal prompts using NER and a local LLM, achieving 93% to 97% F1 on a small synthetic immigration dataset, but the evaluation is undermined by same-model circularity.

  9. Towards a scalable AI-driven framework for data-independent Cyber Threat Intelligence Information Extraction

    cs.CR 2025-01 conditional novelty 4.0 of 10

    0-CTI is a modular system that extracts cybersecurity entities and relationships from threat reports in both supervised and zero-shot modes, reporting higher entity-extraction F1 than the STIXnet baseline.

  10. Text2Insight: Transform natural language text into insights seamlessly using multi-model architecture

    cs.AI 2024-12 reject novelty 3.0 of 10

    Text2Insight combines an LLM text-to-SQL step with a rule-based chart predictor and BERT-based question answering and prediction, but its end-to-end performance claims rest on circular or missing evaluation.

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