# GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface

arXiv: https://arxiv.org/abs/2507.18546 · HTML: https://mlsanity.com/paper/2507.18546/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [GPT-NER: Named Entity Recognition via Large Language Models](https://mlsanity.com/paper/2304.10428/) (2023)
- [MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages](https://mlsanity.com/paper/2204.08582/) (2022)
- [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2012.04373/) (2020)
- [NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data](https://mlsanity.com/paper/2402.15343/) (2024)
- [An In-Depth Investigation of Data Collection in LLM App Ecosystems](https://mlsanity.com/paper/2408.13247/) (2024)
- [GLiREL -- Generalist Model for Zero-Shot Relation Extraction](https://mlsanity.com/paper/2501.03172/) (2025)
- [XNLP: An Interactive Demonstration System for Universal Structured NLP](https://mlsanity.com/paper/2308.01846/) (2023)

## Built on by (most cited first, 9 of 9)

- [MAC: Multi-Agent Constitution Learning](https://mlsanity.com/paper/2603.15968/) (2026)
- [GLiNER Guard: Unified Encoder Family for Production LLM Safety and Privacy](https://mlsanity.com/paper/2605.05277/) (2026)
- [Safer Reasoning Traces: Measuring and Mitigating Chain-of-Thought Leakage in LLMs](https://mlsanity.com/paper/2603.05618/) (2026)
- [ToMMeR -- Efficient Entity Mention Detection from Large Language Models](https://mlsanity.com/paper/2510.19410/) (2025)
- [Stemming Hallucination in Language Models Using a Licensing Oracle](https://mlsanity.com/paper/2511.06073/) (2025)
- [PrionNER: A Named Entity Recognition Dataset for Prion Disease Biomedical Literature](https://mlsanity.com/paper/2605.28375/) (2026)
- [RedactionBench](https://mlsanity.com/paper/2606.18782/) (2026)
- [Where Does the Signal Live? A Web Data Recipe for Medical Encoder Pretraining](https://mlsanity.com/paper/2606.22079/) (2026)
- [DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Vision](https://mlsanity.com/paper/2610.19118/) (2026)

## Abstract (arXiv)

Information extraction (IE) is fundamental to numerous NLP applications, yet existing solutions often require specialized models for different tasks or rely on computationally expensive large language models. We present GLiNER2, a unified framework that enhances the original GLiNER architecture to support named entity recognition, text classification, and hierarchical structured data extraction within a single efficient model. Built pretrained transformer encoder architecture, GLiNER2 maintains CPU efficiency and compact size while introducing multi-task composition through an intuitive schema-based interface. Our experiments demonstrate competitive performance across extraction and classification tasks with substantial improvements in deployment accessibility compared to LLM-based alternatives. We release GLiNER2 as an open-source pip-installable library with pre-trained models and documentation at https://github.com/fastino-ai/GLiNER2.