1 citations · 2 across the 9 of their papers we have counts for
9 papers
MCBench: A Multicontext Safety Assessment Benchmark for Omni Large Language Models
Manh Luong, Tamas Abraham, Junae Kim +6
Existing multimodal safety benchmarks focus solely on visual inputs and cannot assess Omni Large Language Models (LLMs) that process vision, audio, and text. We introduce MCBench,…
TriAlign: Towards Universal Truth Consistency in Personalized LLM Alignment
Thi-Nhung Nguyen, Linhao Luo, Rollin Omari +3
Personalized large language models adapt responses to users' preferences and social attributes, but can introduce substantial universal truth inconsistencies across social groups,…
MATO: Multi-objective Personalized Alignment with Test-time Optimization for Large Language Models
Linhao Luo, Thuy-Trang Vu, Van-Anh Nguyen +3
Aligning large language models (LLMs) with diverse and multifaceted user preferences is a fundamental challenge in personalized AI systems. Existing multi-objective alignment metho…
Adaptive Subspace Projection for Generative Personalization
Van-Anh Nguyen, Anh Tuan Bui, Tamas Abraham +5
Generative personalization often suffers from the semantic collapsing problem (SCP), where a learned personalized concept overpowers the rest of the text prompt, causing the model…
Mitigating Semantic Collapse in Generative Personalization with Test-Time Embedding Adjustment
Anh Bui, Trang Vu, Trung Le +5
In this paper, we investigate the semantic collapsing problem in generative personalization, an under-explored topic where the learned visual concept () gradually shifts from it…
Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them
Anh Bui, Trang Vu, Long Vuong +5
Concept erasure has emerged as a promising technique for mitigating the risk of harmful content generation in diffusion models by selectively unlearning undesirable concepts. The c…
A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles
Junae Kim, Amardeep Kaur
In autonomous driving, the combination of AI and vehicular technology offers great potential. However, this amalgamation comes with vulnerabilities to adversarial attacks. This sur…
EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detection
Shigang Liu, Di Cao, Junae Kim +5
Recently, deep learning has demonstrated promising results in enhancing the accuracy of vulnerability detection and identifying vulnerabilities in software. However, these techniqu…
Adversarial Robustness on Image Classification with -means
Rollin Omari, Junae Kim, Paul Montague
In this paper we explore the challenges and strategies for enhancing the robustness of -means clustering algorithms against adversarial manipulations. We evaluate the vulnerabil…