RetrieveAll: A Multilingual Named Entity Recognition Framework with Large Language Models

• URL: http://arxiv.org/abs/2505.19128v1
• Date: Sun, 25 May 2025 12:52:18 GMT
• Title: RetrieveAll: A Multilingual Named Entity Recognition Framework with Large Language Models
• Authors: Jin Zhang, Fan Gao, Linyu Li, Yongbin Yu, Xiangxiang Wang, Nyima Tashi, Gadeng Luosang,
• Abstract summary: Existing multilingual NER methods face severe language interference during the multi-language adaptation process.<n>We propose RetrieveAll, a universal multilingual NER framework based on dynamic LoRA.<n>We introduce a cross-granularity knowledge augmented method that fully exploits the intrinsic potential of the data.
• Score: 7.867158538366131
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rise of large language models has led to significant performance breakthroughs in named entity recognition (NER) for high-resource languages, yet there remains substantial room for improvement in low- and medium-resource languages. Existing multilingual NER methods face severe language interference during the multi-language adaptation process, manifested in feature conflicts between different languages and the competitive suppression of low-resource language features by high-resource languages. Although training a dedicated model for each language can mitigate such interference, it lacks scalability and incurs excessive computational costs in real-world applications. To address this issue, we propose RetrieveAll, a universal multilingual NER framework based on dynamic LoRA. The framework decouples task-specific features across languages and demonstrates efficient dynamic adaptability. Furthermore, we introduce a cross-granularity knowledge augmented method that fully exploits the intrinsic potential of the data without relying on external resources. By leveraging a hierarchical prompting mechanism to guide knowledge injection, this approach advances the paradigm from "prompt-guided inference" to "prompt-driven learning." Experimental results show that RetrieveAll outperforms existing baselines; on the PAN-X dataset, it achieves an average F1 improvement of 12.1 percent.

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