7 citations · 9 across the 15 of their papers we have counts for
16 papers
DenseSteer: Steering Small Language Models towards Dense Math Reasoning
Yang Ouyang, Shuhang Lin, Jung-Eun Kim
Large language models (LLMs) demonstrate strong chain-of-thought (CoT) reasoning abilities, while smaller models (<= 3B parameters) significantly underperform on multi-step reasoni…
Position: Retire the "Positive Backdoor" Label -- Secret Alignment Requires Strict and Systematic Evaluation
Jianwei Li, Jung-Eun Kim
This position paper argues that the AI/ML community should stop overclaiming and retire the label "positive backdoor," and instead treat trigger-activated hidden behaviors as Secre…
Purifying Generative LLMs from Backdoors without Prior Knowledge or Clean Reference
Jianwei Li, Jung-Eun Kim
Backdoor attacks pose severe security threats to large language models (LLMs), where a model behaves normally under benign inputs but produces malicious outputs when a hidden trigg…
Impact of Layer Norm on Memorization and Generalization in Transformers
Rishi Singhal, Jung-Eun Kim
Layer Normalization (LayerNorm) is one of the fundamental components in transformers that stabilizes training and improves optimization. In recent times, Pre-LayerNorm transformers…
Explaining How Quantization Disparately Skews a Model
Abhimanyu Bellam, Jung-Eun Kim
Post Training Quantization (PTQ) is widely adopted due to its high compression capacity and speed with minimal impact on accuracy. However, we observed that disparate impacts are e…
Severing Spurious Correlations with Data Pruning
Varun Mulchandani, Jung-Eun Kim
Deep neural networks have been shown to learn and rely on spurious correlations present in the data that they are trained on. Reliance on such correlations can cause these networks…
Trustworthy AI: Safety, Bias, and Privacy -- A Survey
Xingli Fang, Jianwei Li, Varun Mulchandani +1
The capabilities of artificial intelligence systems have been advancing to a great extent, but these systems still struggle with failure modes, vulnerabilities, and biases. In this…
Superficial Safety Alignment Hypothesis
Jianwei Li, Jung-Eun Kim
As large language models (LLMs) are overwhelmingly more and more integrated into various applications, ensuring they generate safe responses is a pressing need. Previous studies on…
Estimating Environmental Cost Throughout Model's Adaptive Life Cycle
Vishwesh Sangarya, Richard Bradford, Jung-Eun Kim
With the rapid increase in the research, development, and application of neural networks in the current era, there is a proportional increase in the energy needed to train and use…
Representation Magnitude has a Liability to Privacy Vulnerability
Xingli Fang, Jung-Eun Kim
The privacy-preserving approaches to machine learning (ML) models have made substantial progress in recent years. However, it is still opaque in which circumstances and conditions…
Center-Based Relaxed Learning Against Membership Inference Attacks
Xingli Fang, Jung-Eun Kim
Membership inference attacks (MIAs) are currently considered one of the main privacy attack strategies, and their defense mechanisms have also been extensively explored. However, t…
Aggregate Representation Measure for Predictive Model Reusability
Vishwesh Sangarya, Richard Bradford, Jung-Eun Kim
In this paper, we propose a predictive quantifier to estimate the retraining cost of a trained model in distribution shifts. The proposed Aggregated Representation Measure (ARM) qu…
ReffAKD: Resource-efficient Autoencoder-based Knowledge Distillation
Divyang Doshi, Jung-Eun Kim
In this research, we propose an innovative method to boost Knowledge Distillation efficiency without the need for resource-heavy teacher models. Knowledge Distillation trains a sma…
The Over-Certainty Phenomenon in Modern Test-Time Adaptation Algorithms
Fin Amin, Jung-Eun Kim
When neural networks are confronted with unfamiliar data that deviate from their training set, this signifies a domain shift. While these networks output predictions on their input…
Cooperative Learning for Cost-Adaptive Inference
Xingli Fang, Richard Bradford, Jung-Eun Kim
We propose a cooperative training framework for deep neural network architectures that enables the runtime network depths to change to satisfy dynamic computing resource requiremen…
Pruning has a disparate impact on model accuracy
Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim +1
Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy. This paper shows that prunin…