# ToNER: Type-oriented Named Entity Recognition with Generative Language Model

arXiv: https://arxiv.org/abs/2404.09145 · HTML: https://mlsanity.com/paper/2404.09145/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 48 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [Revisiting Relation Extraction in the era of Large Language Models](https://mlsanity.com/paper/2305.05003/) (2023)
- [Unified Named Entity Recognition as Word-Word Relation Classification](https://mlsanity.com/paper/2112.10070/) (2021)
- [CrossWeigh: Training Named Entity Tagger from Imperfect Annotations](https://mlsanity.com/paper/1909.01441/) (2019)
- [Neural Architectures for Nested NER through Linearization](https://mlsanity.com/paper/1908.06926/) (2019)
- [SpanNER: Named Entity Re-/Recognition as Span Prediction](https://mlsanity.com/paper/2106.00641/) (2021)
- [Fast and Accurate Entity Recognition with Iterated Dilated Convolutions](https://mlsanity.com/paper/1702.02098/) (2017)
- [Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks](https://mlsanity.com/paper/1906.03783/) (2019)
- [An Effective Transition-based Model for Discontinuous NER](https://mlsanity.com/paper/2004.13454/) (2020)
- [A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2106.14373/) (2021)
- [Neural Segmental Hypergraphs for Overlapping Mention Recognition](https://mlsanity.com/paper/1810.01817/) (2018)
- [LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting](https://mlsanity.com/paper/2109.00720/) (2021)
- [BioFLAIR: Pretrained Pooled Contextualized Embeddings for Biomedical Sequence Labeling Tasks](https://mlsanity.com/paper/1908.05760/) (2019)

## Built on by (most cited first, 8 of 8)

- [VCRL: Variance-based Curriculum Reinforcement Learning for Large Language Models](https://mlsanity.com/paper/2509.19803/) (2025)
- [Open (Clinical) LLMs are Sensitive to Instruction Phrasings](https://mlsanity.com/paper/2407.09429/) (2024)
- [FlowKV: A Disaggregated Inference Framework with Low-Latency KV Cache Transfer and Load-Aware Scheduling](https://mlsanity.com/paper/2504.03775/) (2025)
- [P-ICL: Point In-Context Learning for Named Entity Recognition with Large Language Models](https://mlsanity.com/paper/2405.04960/) (2024)
- [ANCHOLIK-NER: A Benchmark Dataset for Bangla Regional Named Entity Recognition](https://mlsanity.com/paper/2502.11198/) (2025)
- [Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy](https://mlsanity.com/paper/2412.08434/) (2024)
- [Redact or Keep? A Fully Local AI Cascade for Educational Dialogue De-Identification](https://mlsanity.com/paper/2606.18372/) (2026)
- [Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approach](https://mlsanity.com/paper/2608.08636/) (2026)

## Abstract (arXiv)

In recent years, the fine-tuned generative models have been proven more powerful than the previous tagging-based or span-based models on named entity recognition (NER) task. It has also been found that the information related to entities, such as entity types, can prompt a model to achieve NER better. However, it is not easy to determine the entity types indeed existing in the given sentence in advance, and inputting too many potential entity types would distract the model inevitably. To exploit entity types' merit on promoting NER task, in this paper we propose a novel NER framework, namely ToNER based on a generative model. In ToNER, a type matching model is proposed at first to identify the entity types most likely to appear in the sentence. Then, we append a multiple binary classification task to fine-tune the generative model's encoder, so as to generate the refined representation of the input sentence. Moreover, we add an auxiliary task for the model to discover the entity types which further fine-tunes the model to output more accurate results. Our extensive experiments on some NER benchmarks verify the effectiveness of our proposed strategies in ToNER that are oriented towards entity types' exploitation.