# The Million-Label NER: Breaking Scale Barriers with GLiNER bi-encoder

arXiv: https://arxiv.org/abs/2602.18487 · HTML: https://mlsanity.com/paper/2602.18487/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://mlsanity.com/paper/2311.08526/) (2023)
- [Seq vs Seq: An Open Suite of Paired Encoders and Decoders](https://mlsanity.com/paper/2507.11412/) (2025)
- [Business Document Information Extraction: Towards Practical Benchmarks](https://mlsanity.com/paper/2206.11229/) (2022)
- [GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition](https://mlsanity.com/paper/2504.00676/) (2025)

## Built on by (most cited first, 5 of 5)

- [Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference](https://mlsanity.com/paper/2604.19566/) (2026)
- [GLiNER Guard: Unified Encoder Family for Production LLM Safety and Privacy](https://mlsanity.com/paper/2605.05277/) (2026)
- [GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extraction](https://mlsanity.com/paper/2605.10108/) (2026)
- [LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation](https://mlsanity.com/paper/2605.26956/) (2026)
- [Follow the Entities: A Corpus Map for Agentic Search](https://mlsanity.com/paper/2609.37226/) (2026)

## Abstract (arXiv)

This paper introduces GLiNER-bi-Encoder, a novel architecture for Named Entity Recognition (NER) that harmonizes zero-shot flexibility with industrial-scale efficiency. While the original GLiNER framework offers strong generalization, its joint-encoding approach suffers from quadratic complexity as the number of entity labels increases. Our proposed bi-encoder design decouples the process into a dedicated label encoder and a context encoder, effectively removing the context-window bottleneck. This architecture enables the simultaneous recognition of thousands, and potentially millions, of entity types with minimal overhead. Experimental results demonstrate state-of-the-art zero-shot performance, achieving 61.5 percent Micro-F1 on the CrossNER benchmark. Crucially, by leveraging pre-computed label embeddings, GLiNER-bi-Encoder achieves up to a 130 times throughput improvement at 1024 labels compared to its uni-encoder predecessors. Furthermore, we introduce GLiNKER, a modular framework that leverages this architecture for high-performance entity linking across massive knowledge bases such as Wikidata.