4 citations · 15 across the 14 of their papers we have counts for
9 papers · 1 filter
Token Highlighter: Inspecting and Mitigating Jailbreak Prompts for Large Language Models
Xiaomeng Hu, Pin-Yu Chen, Tsung-Yi Ho
Large Language Models (LLMs) are increasingly being integrated into services such as ChatGPT to provide responses to user queries. To mitigate potential harm and prevent misuse, th…
Retention Score: Quantifying Jailbreak Risks for Vision Language Models
Zaitang Li, Pin-Yu Chen, Tsung-Yi Ho
The emergence of Vision-Language Models (VLMs) is a significant advancement in integrating computer vision with Large Language Models (LLMs) to enhance multi-modal machine learning…
Defining and Evaluating Physical Safety for Large Language Models
Yung-Chen Tang, Pin-Yu Chen, Tsung-Yi Ho
Large Language Models (LLMs) are increasingly used to control robotic systems such as drones, but their risks of causing physical threats and harm in real-world applications remain…
When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood Perspective
Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen +1
Adapting pre-trained models to new tasks can exhibit varying effectiveness across datasets. Visual prompting, a state-of-the-art parameter-efficient transfer learning method, can s…
The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Pre-trained Language Models
Yan Liu, Yu Liu, Xiaokang Chen +4
Pre-trained Language models (PLMs) have been acknowledged to contain harmful information, such as social biases, which may cause negative social impacts or even bring catastrophic…
RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection
Zhiyuan He, Pin-Yu Chen, Tsung-Yi Ho
The rapid advances in generative AI models have empowered the creation of highly realistic images with arbitrary content, raising concerns about potential misuse and harm, such as…
Defensive Prompt Patch: A Robust and Interpretable Defense of LLMs against Jailbreak Attacks
Chen Xiong, Xiangyu Qi, Pin-Yu Chen +1
Safety, security, and compliance are essential requirements when aligning large language models (LLMs). However, many seemingly aligned LLMs are soon shown to be susceptible to jai…
NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein Classification in Graph Neural Networks
Yi-Shan Lan, Pin-Yu Chen, Tsung-Yi Ho
Protein classification tasks are essential in drug discovery. Real-world protein structures are dynamic, which will determine the properties of proteins. However, the existing mach…
Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes
Xiaomeng Hu, Pin-Yu Chen, Tsung-Yi Ho
Large Language Models (LLMs) are becoming a prominent generative AI tool, where the user enters a query and the LLM generates an answer. To reduce harm and misuse, efforts have bee…