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The proposed framework uses a shared-specific Mixture-of-LoRA architecture comprising one shared LoRA and six task-specific expert LoRAs, together with a two-stage training procedure, and achieves good results in the FLARE 2026 Task 3 test sets.
The Global-Local Multi-Source Domain Network (GLMD-Net) for LUS analysis in data-scarce scenarios is developed, which first employs the proposed Average Nearest Neighbor Set Distance (ANNSD) to prioritize relevant source domains for pre-training and harmonizes multi-source gradient updates.
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