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Found 19 papers, 10 papers with code
Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination
The widespread use of these fixed benchmark datasets makes the benchmarking process to be static and thus particularly susceptible to data contamination, an unavoidable consequence of the extensive data collection processes used to train Code LLMs.
Recent Advances in Large Langauge Model Benchmarks against Data Contamination: From Static to Dynamic Evaluation
To mitigate the risk of potential data contamination, LLM benchmarking has undergone a transformation from static to dynamic benchmarking.
Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment
These results highlight ED-TOOLBOX as a superior solution for edge object detection.
FedDTPT: Federated Discrete and Transferable Prompt Tuning for Black-Box Large Language Models
To address these challenges, we propose for the first time a federated discrete and transferable prompt tuning, namely FedDTPT, for black-box large language models.
PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models
In recent times, a plethora of Large Code Generation Models (LCGMs) have been proposed, showcasing significant potential in assisting developers with complex programming tasks.
Uncertainty Awareness of Large Language Models Under Code Distribution Shifts: A Benchmark Study
We observe that these methods generally improve the uncertainty awareness of CodeLlama, with increased calibration quality and higher uncertainty estimation~(UE) precision.
DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization
\tool tackles the dynamic nature of DyNNs by introducing a compilation mechanism that redistributes the control and data flow of the original DNN programs during the compilation process.
SlothSpeech: Denial-of-service Attack Against Speech Recognition Models
We show that popular ASR models like Speech2Text model and Whisper model have dynamic computation based on different inputs, causing dynamic efficiency.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Dynamic Transformers Provide a False Sense of Efficiency
Despite much success in natural language processing (NLP), pre-trained language models typically lead to a high computational cost during inference.
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition
A hard challenge in developing practical face recognition (FR) attacks is due to the black-box nature of the target FR model, i. e., inaccessible gradient and parameter information to attackers.
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection
Recent advancements in deploying deep neural networks (DNNs) on resource-constrained devices have generated interest in input-adaptive dynamic neural networks (DyNNs).
DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks
To ensure an AdNN satisfies the performance requirements of resource-constrained applications, it is essential to conduct performance testing to detect IDPBs in the AdNN.
LLMEffiChecker: Understanding and Testing Efficiency Degradation of Large Language Models
In this paper, we make the first attempt to understand and test potential computation efficiency robustness in state-of-the-art LLMs.
Learning to Reverse DNNs from AI Programs Automatically
With the privatization deployment of DNNs on edge devices, the security of on-device DNNs has raised significant concern.
NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models
To further understand such efficiency-oriented threats, we propose a new attack approach, NICGSlowDown, to evaluate the efficiency robustness of NICG models.
TransSlowDown: Efficiency Attacks on Neural Machine Translation Systems
To further the understanding of such efficiency-oriented threats and raise the community’s concern on the efficiency robustness of NMT systems, we propose a new attack approach, TranSlowDown, to test the efficiency robustness of NMT systems.
NODEAttack: Adversarial Attack on the Energy Consumption of Neural ODEs
Unlike the memory cost, the energy consumption of the Neural ODEs during inference can be adaptive because of the adaptive nature of the ODE solvers.
Estimating Predictive Uncertainty Under Program Data Distribution Shift
Experiments show that program distribution shift does degrade the DL model performance to varying degrees and that existing uncertainty methods all present certain limitations in quantifying uncertainty on program dataset.
AttackDist: Characterizing Zero-day Adversarial Samples by Counter Attack
We first theoretically clarify under which condition AttackDist can provide a certified detecting performance, then show that a potential application of AttackDist is distinguishing zero-day adversarial examples without knowing the mechanisms of new attacks.
