PEML co-optimizes continuous prompts and low-rank adaptations to deliver up to 6.67% average accuracy gains over existing multi-task PEFT methods on GLUE, SuperGLUE, and other benchmarks.
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CovNorm reduces parameters in domain-adaptive layers via two PCAs and a mini-adaptation layer, enabling efficient multi-domain learning with performance close to full fine-tuning.
A multitask learning framework with soft parameter sharing between classification and regression tasks detects blackmarket tweets at F1-score 0.89.
citing papers explorer
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PEML: Parameter-efficient Multi-Task Learning with Optimized Continuous Prompts
PEML co-optimizes continuous prompts and low-rank adaptations to deliver up to 6.67% average accuracy gains over existing multi-task PEFT methods on GLUE, SuperGLUE, and other benchmarks.
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Efficient Multi-Domain Network Learning by Covariance Normalization
CovNorm reduces parameters in domain-adaptive layers via two PCAs and a mini-adaptation layer, enabling efficient multi-domain learning with performance close to full fine-tuning.
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Multitask Learning for Blackmarket Tweet Detection
A multitask learning framework with soft parameter sharing between classification and regression tasks detects blackmarket tweets at F1-score 0.89.