NE-R1: Enhancing Named Entity Recognition Model via Reinforcement Learning

• URL: http://arxiv.org/abs/2609.02366v1
• Date: Wed, 02 Sep 2026 09:37:16 GMT
• Title: NE-R1: Enhancing Named Entity Recognition Model via Reinforcement Learning
• Abstract summary: We propose NE-R1, a novel framework for adaptive retrieval-augmented NER.<n>We design a "retrieval-on-demand" mechanism for NER. Then we integrate it into models by a two-stage training method.<n> NE-R1 achieves state-of-the-art performance on various benchmarks, with an average F1 score gain of 2.52% in in-domain evaluation and 1.18% in zero-shot cross-domain evaluation.
• Score: 12.75939447499315
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 an average F1 score gain of 2.52% in in-domain evaluation and 1.18% in zero-shot cross-domain evaluation.

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