@inproceedings{chen-etal-2026-ne,
title = "{NE}-{R}1: Enhancing Named Entity Recognition Model via Reinforcement Learning",
author = "Chen, Meixuan and
Li, Hehan and
Zhao, Ruizhi and
Lu, Xin and
xu, Peizhi and
Qian, Liwei and
Meifang, LI and
li, Shuanglong and
Liu, Hanmeng and
Pei, Xin and
Ma, Yanbiao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2025/",
pages = "46132--46149",
ISBN = "979-8-89176-459-0",
abstract = "Named Entity Recognition (NER) has achieved substantial progress since the advent of large language models (LLMs). Nevertheless, the recognition of long-tail and domain-specific entities remains challenging due to the deficiency in parametric knowledge. Retrieval-augmented generation (RAG) offers a promising remedy by injecting external knowledge, but it also introduces noise and unnecessary cost when dealing with familiar cases. In this paper, we propose NE-R1, a novel framework for adaptive retrieval-augmented NER. We design a ``retrieval-on-demand'' mechanism for NER. Then we integrate it into models by a two-stage training method: (1) multi-task instruction tuning initialization; (2) end-to-end RL optimization with CoT. To achieve reasonable selection between parameterized and external knowledge, we design a multi-dimensional reward considering both accuracy and retrieval benefit. NE-R1 achieves state-of-the-art performance on various benchmarks, with average improvements of 2.52 F1 points in in-domain evaluation and 1.18 F1 points in zero-shot cross-domain evaluation."
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<abstract>Named Entity Recognition (NER) has achieved substantial progress since the advent of large language models (LLMs). Nevertheless, the recognition of long-tail and domain-specific entities remains challenging due to the deficiency in parametric knowledge. Retrieval-augmented generation (RAG) offers a promising remedy by injecting external knowledge, but it also introduces noise and unnecessary cost when dealing with familiar cases. In this paper, we propose NE-R1, a novel framework for adaptive retrieval-augmented NER. We design a “retrieval-on-demand” mechanism for NER. Then we integrate it into models by a two-stage training method: (1) multi-task instruction tuning initialization; (2) end-to-end RL optimization with CoT. To achieve reasonable selection between parameterized and external knowledge, we design a multi-dimensional reward considering both accuracy and retrieval benefit. NE-R1 achieves state-of-the-art performance on various benchmarks, with average improvements of 2.52 F1 points in in-domain evaluation and 1.18 F1 points in zero-shot cross-domain evaluation.</abstract>
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%0 Conference Proceedings
%T NE-R1: Enhancing Named Entity Recognition Model via Reinforcement Learning
%A Chen, Meixuan
%A Li, Hehan
%A Zhao, Ruizhi
%A Lu, Xin
%A xu, Peizhi
%A Qian, Liwei
%A Meifang, L. I.
%A li, Shuanglong
%A Liu, Hanmeng
%A Pei, Xin
%A Ma, Yanbiao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F chen-etal-2026-ne
%X Named Entity Recognition (NER) has achieved substantial progress since the advent of large language models (LLMs). Nevertheless, the recognition of long-tail and domain-specific entities remains challenging due to the deficiency in parametric knowledge. Retrieval-augmented generation (RAG) offers a promising remedy by injecting external knowledge, but it also introduces noise and unnecessary cost when dealing with familiar cases. In this paper, we propose NE-R1, a novel framework for adaptive retrieval-augmented NER. We design a “retrieval-on-demand” mechanism for NER. Then we integrate it into models by a two-stage training method: (1) multi-task instruction tuning initialization; (2) end-to-end RL optimization with CoT. To achieve reasonable selection between parameterized and external knowledge, we design a multi-dimensional reward considering both accuracy and retrieval benefit. NE-R1 achieves state-of-the-art performance on various benchmarks, with average improvements of 2.52 F1 points in in-domain evaluation and 1.18 F1 points in zero-shot cross-domain evaluation.
%U https://aclanthology.org/2026.emnlp-main.2025/
%P 46132-46149
Markdown (Informal)
[NE-R1: Enhancing Named Entity Recognition Model via Reinforcement Learning](https://aclanthology.org/2026.emnlp-main.2025/) (Chen et al., EMNLP 2026)
ACL
- Meixuan Chen, Hehan Li, Ruizhi Zhao, Xin Lu, Peizhi xu, Liwei Qian, LI Meifang, Shuanglong li, Hanmeng Liu, Xin Pei, and Yanbiao Ma. 2026. NE-R1: Enhancing Named Entity Recognition Model via Reinforcement Learning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 46132–46149, Budapest, Hungary. Association for Computational Linguistics.