The paper evaluates open‑source large language models (LLMs) for both flat and nested named entity recognition (NER) tasks, comparing them to traditional encoder‑based models and decoder‑based LLMs using in‑context learning. It shows that with parameter‑efficient fine‑tuning and structured output formats (e.g., inline brackets or XML), LLMs can match or exceed the performance of conventional NER systems, and that their NER abilities arise from instruction following rather than memorization. Additionally, fine‑tuning for NER minimally affects, and can even improve, the LLMs’ general capabilities on other benchmarks.
arXiv:2601.06347v3 Announce Type: replace Abstract: Recent progress in universal multilingual named entity recognition (NER) has been driven by multilingual transformer models, task-specific architec...
The paper compares generative and encoder-based neural models for multilingual Named Entity Recognition (NER) across the eleven languages of the Naamapadam benchmark. Five classic model families, four decoder-only large language models fine‑tuned with LoRA and 4‑bit NF4 quantisation, and nine generative models in zero‑to‑5‑shot inference were evaluated under strict CoNLL span‑level metrics. Encoder-based models (mBERT and XLM‑R) achieved substantially higher F1 scores—up to 0.675 on Hindi—than any generative architecture, with gaps of 7.5–40 percentage points; the best few‑shot result reached only 28% of the encoder baseline. The study identifies three language clusters (encoder‑dominant, partial‑coverage, and failure‑zone) and offers deployment guidelines based on transfer learning and low‑resource NLP principles.
The paper introduces a scalable, multi-step framework designed to improve the quality of Named Entity Recognition (NER) annotations, particularly in low-resource languages. It employs a frequency-based iterative approach that combines self‑training with a dual‑threshold mechanism to increase inference confidence. Experiments on various NER datasets show notable performance gains over the original data, and the study also investigates the use of generative Large Language Models for NER tasks.
The paper investigates whether adding Abstract Meaning Representation (AMR) data to large language models (LLMs) improves performance on downstream tasks. By reproducing recent studies and applying a consistent hyperparameter protocol, the authors find that text-only baselines match or surpass AMR-augmented models. A perplexity-based probe shows that AMR does not provide LLMs with additional relational knowledge, suggesting no clear benefit from AMR augmentation.
Large language models (LLMs) are increasingly used to annotate datasets for training smaller, task‑specialized models such as named entity recognition (NER). However, current fine‑tuning processes ignore the annotation noise introduced by LLMs, leading to degraded performance, and existing noise‑robust losses fail to handle the heterogeneous nature of NER noise (e.g., missing mentions vs. type errors). The authors propose error‑type‑aware loss reweighting, which applies separate reweighting rules for different erroneous token types, improving F1 scores by 0.8–2.0 percentage points on average and up to 4.6 points on Wikigold at 24.1% noise.
Just Pass Twice (JPT) is a method that allows causal large language models to perform token classification for zero‑shot named entity recognition by concatenating the input with itself, giving each token full bidirectional context without architectural changes. The approach combines these representations with definition‑guided entity embeddings to enable flexible zero‑shot generalization. JPT achieves state‑of‑the‑art results, outperforming prior methods by an average of +7.9 F1 on CrossNER and MIT benchmarks and running over 20× faster than comparable generative approaches.
arXiv:2510. 20535v2 Announce Type: replace-cross Abstract: Recent techniques such as retrieval-augmented generation or chain-of-thought reasoning have led to longer contexts and increased inference costs.
arXiv:2601. 22146v2 Announce Type: replace-cross Abstract: Due to limited supervised training data, large language models (LLMs) are typically pre-trained via a self-supervised "predict the next word" objective on a vast amount of unstructured text data.
NE‑R1 is a framework that improves Named Entity Recognition by using a retrieval‑on‑demand mechanism and a two‑stage training process that includes instruction tuning and reinforcement learning with chain‑of‑thought. It balances the use of internal model parameters and external knowledge through a multi‑dimensional reward that considers accuracy and retrieval benefit. The approach achieves state‑of‑the‑art results, improving in‑domain F1 by 2.52% and zero‑shot cross‑domain F1 by 1.18%.
arXiv:2606. 10392v1 Announce Type: new Abstract: Financial named-entity recognition (NER) is essential for translating unstructured financial reports and news into structured knowledge graphs.
The paper introduces MITE, a method that transforms biomedical named entity recognition (BioNER) into a structure‑to‑structure generation task by encoding instructions and outputs in multiple programming languages (Python, C++, Java). This approach provides structurally diverse supervision without extra biomedical knowledge, and during inference it aggregates predictions via entity‑level voting to reduce language‑specific variance. Experiments on six BioNER datasets show that MITE outperforms BERT‑based and LLM‑based baselines and generalizes well across datasets.
The paper proposes an information-weighted cross‑entropy loss that rescales token contributions using TF‑IDF statistics, thereby emphasizing semantically informative tokens and down‑weighting ubiquitous ones. Experiments on five decoder‑only language models (1.1B–13B parameters) show consistent reductions in memorized substring length while maintaining perplexity and downstream performance. The method is architecture‑agnostic, adds less than 3% computational overhead, and can be integrated into existing training pipelines.