98 citations · 156 across the 13 of their papers we have counts for
13 papers
PromptKeeper: Safeguarding System Prompts for LLMs
Zhifeng Jiang, Zhihua Jin, Guoliang He
System prompts are widely used to guide the outputs of large language models (LLMs). These prompts often contain business logic and sensitive information, making their protection e…
JailbreakHunter: A Visual Analytics Approach for Jailbreak Prompts Discovery from Large-Scale Human-LLM Conversational Datasets
Zhihua Jin, Shiyi Liu, Haotian Li +2
Large Language Models (LLMs) have gained significant attention but also raised concerns due to the risk of misuse. Jailbreak prompts, a popular type of adversarial attack towards L…
Towards an Exploratory Visual Analytics System for Griefer Identification in MOBA Games
Zixin Chen, Shiyi Liu, Zhihua Jin +5
Multiplayer Online Battle Arenas (MOBAs) have gained a significant player base worldwide, generating over two billion US dollars in annual game revenue. However, the presence of gr…
CommonsenseVIS: Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language Models
Xingbo Wang, Renfei Huang, Zhihua Jin +2
Recently, large pretrained language models have achieved compelling performance on commonsense benchmarks. Nevertheless, it is unclear what commonsense knowledge the models learn a…
ActorLens: Visual Analytics for High-level Actor Identification in MOBA Games
Zhihua Jin, Gaoping Huang, Zixin Chen +5
Multiplayer Online Battle Arenas (MOBAs) have garnered a substantial player base worldwide. Nevertheless, the presence of noxious players, commonly referred to as "actors", can sig…
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions
Lingjiao Chen, Zhihua Jin, Sabri Eyuboglu +3
Commercial ML APIs offered by providers such as Google, Amazon and Microsoft have dramatically simplified ML adoption in many applications. Numerous companies and academics pay to…
ShortcutLens: A Visual Analytics Approach for Exploring Shortcuts in Natural Language Understanding Dataset
Zhihua Jin, Xingbo Wang, Furui Cheng +3
Benchmark datasets play an important role in evaluating Natural Language Understanding (NLU) models. However, shortcuts -- unwanted biases in the benchmark datasets -- can damage t…
NumGPT: Improving Numeracy Ability of Generative Pre-trained Models
Zhihua Jin, Xin Jiang, Xingbo Wang +4
Existing generative pre-trained language models (e.g., GPT) focus on modeling the language structure and semantics of general texts. However, those models do not consider the numer…
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis
Xingbo Wang, Jianben He, Zhihua Jin +3
Multimodal sentiment analysis aims to recognize people's attitudes from multiple communication channels such as verbal content (i.e., text), voice, and facial expressions. It has b…
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
Zhixian Chen, Tengfei Ma, Zhihua Jin +2
Graph convolutional networks (GCNs) have achieved great success on graph-structured data. Many graph convolutional networks can be thought of as low-pass filters for graph signals.…
GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks
Zhihua Jin, Yong Wang, Qianwen Wang +3
Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in rece…
DeepDrawing: A Deep Learning Approach to Graph Drawing
Yong Wang, Zhihua Jin, Qianwen Wang +3
Node-link diagrams are widely used to facilitate network explorations. However, when using a graph drawing technique to visualize networks, users often need to tune different algor…
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
Qianwen Wang, Yao Ming, Zhihua Jin +5
To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically s…