Year: 2026
Type: conference-paper
Abstract: The rise of large language models has led to breakthroughs in named entity recognition for high-resource languages, yet substantial room for improvement remains in low- and medium-resource languages. Existing multilingual named entity recognition 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 named entity recognition 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. Experimental results show that RetrieveAll achieves an average F1 improvement of 12.1% on the PAN-X dataset.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4414585231
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