7 accepted papers
Large Language Models (LLMs) have shown to be vulnerable to backdoor attacks, yet we observe that many LLM backdoors do not survive when end users perform supervised fine-tuning (SFT). In this work, we provide a geometric explanation: by probing the backdoor objective under controlled weight perturb…
Backdoor attacks pose a significant threat to machine learning models, allowing adversaries to implant hidden triggers that alter model behavior when activated. Although gradient ascent (GA)-based unlearning has been proposed as an efficient backdoor removal approach, we identify a critical yet over…
Large Language Models (LLMs) excel in Natural Language Processing (NLP) tasks but often propagate societal biases from their training data, leading to discriminatory outputs. These biases are amplified by the models’ self-attention mechanisms, which disproportionately emphasize biased correlations w
Capturing and visualizing motion using skeleton-based techniques is a key aspect of computer vision, particularly in virtual reality (VR) settings. Its popularity has surged, driven by the simplicity of obtaining skeleton data and the growing appetite for virtual interaction. Although this skeleton…
Pre-trained language models (PLMs) are commonly used for various downstream natural language processing tasks via fine-tuning. However, recent studies have demonstrated that PLMs are vulnerable to backdoor attacks, which can mislabel poisoned samples to target outputs even after a vanilla fine-tunin…
As the field of Natural Language Processing (NLP) increasingly adopts transformer-based models, the issue of bias becomes more pronounced. Such bias, manifesting through stereotypes and discriminatory practices, can disadvantage certain groups. Our study focuses on direct and indirect bias in the mo…
Many word-level adversarial attack approaches for textual data have been proposed in recent studies. However, due to the massive search space consisting of combinations of candidate words, the existing approaches face the problem of preserving the semantics of texts when crafting adversarial counter…