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Found 5 papers, 2 papers with code
Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition
First, we leverage parameter-efficient prompt tuning to drive the inputted arguments to match the pre-trained space and realize the approximation with few parameters.
Revisiting the Information Capacity of Neural Network Watermarks: Upper Bound Estimation and Beyond
To trace the copyright of deep neural networks, an owner can embed its identity information into its model as a watermark.
ShiftNAS: Improving One-shot NAS via Probability Shift
To address the problem of uniform sampling, we propose ShiftNAS, a method that can adjust the sampling probability based on the complexity of subnets.
UOR: Universal Backdoor Attacks on Pre-trained Language Models
In this paper, we first summarize the requirements that a more threatening backdoor attack against PLMs should satisfy, and then propose a new backdoor attack method called UOR, which breaks the bottleneck of the previous approach by turning manual selection into automatic optimization.
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning
In this paper, we propose "FedPrompt" to study prompt tuning in a model split aggregation way using FL, and prove that split aggregation greatly reduces the communication cost, only 0. 01% of the PLMs' parameters, with little decrease on accuracy both on IID and Non-IID data distribution.
