What Matters When Building Universal Multilingual Named Entity Recognition Models?

arXiv:2601.06347 · cs.CL · Submitted 2026-01-09 · Read on arXiv

cs.CL

Submitted: 2026-01-09

Updated: 2026-08-30

Terminology

• Transformer — 24× in this paper · explained in 2 episodes
• Loss Function — 13× in this paper · explained in 2 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• Embeddings — 9× in this paper · explained in 2 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• F1 Score — 5× in this paper · explained in 3 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Core — 3× in this paper · explained in 2 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Graph Construction — 3× in this paper · explained in 3 episodes
• Named Entity Recognition (NER) — 3× in this paper · explained in 2 episodes
• Transformer Models — 3× in this paper · explained in 2 episodes
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Knowledge Distillation — 2× in this paper · explained in 11 episodes
• Knowledge Graph — 2× in this paper · explained in 7 episodes
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Elias — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Low-Resource Languages — 1× in this paper · explained in 4 episodes
• Ontology — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• PromptNER: Prompting For Named Entity Recognition
• Language Models are Few-Shot Learners
• Autoregressive Entity Retrieval
• DeepSeek-V3 Technical Report
• FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition
• The Llama 3 Herd of Models
• DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
• Distilling the Knowledge in a Neural Network
• Focal Loss for Dense Object Detection
• Decoupled Weight Decay Regularization
• Universal Information Extraction as Unified Semantic Matching
• mmBERT: A Modern Multilingual Encoder with Annealed Language Learning
• Gemma 3 Technical Report
• InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction
• Qwen3 Technical Report
• UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering