A gated-adapter method with a sparsity regularizer is reported to match or slightly beat full fine-tuning on MNLI and BoolQ using 1.4% of parameters, but lacks the experimental detail needed to verify the claim.
Vision-Oriented Multi-Object Tracking via Transformer-Based Temporal and Attention Modeling,
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Structure-Learnable Adapter Fine-Tuning for Parameter-Efficient Large Language Models
A gated-adapter method with a sparsity regularizer is reported to match or slightly beat full fine-tuning on MNLI and BoolQ using 1.4% of parameters, but lacks the experimental detail needed to verify the claim.