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Found 11 papers, 5 papers with code
When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning
Large reasoning models (LRMs) achieve remarkable performance via long reasoning chains, but often incur excessive computational overhead due to redundant reasoning, especially on simple tasks.
FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing
To address this gap, we propose a realistic federated RS dataset, termed FedRS.
Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language Models
Multi-Modal Large Language Models (MLLMs) have exhibited remarkable performance on various vision-language tasks such as Visual Question Answering (VQA).
Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly
Based on these observations, we outline key research directions aimed at bridging the gap between current VFL research and real-world applications.
NSmark: Null Space Based Black-box Watermarking Defense Framework for Language Models
Language models (LMs) have emerged as critical intellectual property (IP) assets that necessitate protection.
Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities
The rapid adoption of large language models (LLMs) in multi-agent systems has highlighted their impressive capabilities in various applications, such as collaborative problem-solving and autonomous negotiation.
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.
