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    <abstract>Despite the recent success of two-stage prototypical networks in few-shot named entity recognition (NER), challenges such as over/under-detected false spans in the span detection stage and unaligned entity prototypes in the type classification stage persist. Additionally, LLMs have not proven to be effective few-shot information extractors in general. In this paper, we propose an approach called Boundary-Aware LLMs for Few-Shot Named Entity Recognition to address these issues. We introduce a boundary-aware contrastive learning strategy to enhance the LLM‘s ability to perceive entity boundaries for generalized entity spans. Additionally, we utilize LoRAHub to align information from the target domain to the source domain, thereby enhancing adaptive cross-domain classification capabilities. Extensive experiments across various benchmarks demonstrate that our framework outperforms prior methods, validating its effectiveness. In particular, the proposed strategies demonstrate effectiveness across a range of LLM architectures. The code and data are released on https://github.com/UESTC-GQJ/BANER.</abstract>
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%0 Conference Proceedings
%T BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition
%A Guo, Quanjiang
%A Dong, Yihong
%A Tian, Ling
%A Kang, Zhao
%A Zhang, Yu
%A Wang, Sijie
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F guo-etal-2025-baner
%X Despite the recent success of two-stage prototypical networks in few-shot named entity recognition (NER), challenges such as over/under-detected false spans in the span detection stage and unaligned entity prototypes in the type classification stage persist. Additionally, LLMs have not proven to be effective few-shot information extractors in general. In this paper, we propose an approach called Boundary-Aware LLMs for Few-Shot Named Entity Recognition to address these issues. We introduce a boundary-aware contrastive learning strategy to enhance the LLM‘s ability to perceive entity boundaries for generalized entity spans. Additionally, we utilize LoRAHub to align information from the target domain to the source domain, thereby enhancing adaptive cross-domain classification capabilities. Extensive experiments across various benchmarks demonstrate that our framework outperforms prior methods, validating its effectiveness. In particular, the proposed strategies demonstrate effectiveness across a range of LLM architectures. The code and data are released on https://github.com/UESTC-GQJ/BANER.
%U https://aclanthology.org/2025.coling-main.691/
%P 10375-10389

Markdown (Informal)

[BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition](https://aclanthology.org/2025.coling-main.691/) (Guo et al., COLING 2025)

• BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition (Guo et al., COLING 2025)

ACL

• Quanjiang Guo, Yihong Dong, Ling Tian, Zhao Kang, Yu Zhang, and Sijie Wang. 2025. BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition. In Proceedings of the 31st International Conference on Computational Linguistics, pages 10375–10389, Abu Dhabi, UAE. Association for Computational Linguistics.