22 citations · 65 across the 14 of their papers we have counts for
8 papers · 1 filter
FlipAttack: Jailbreak LLMs via Flipping
Yue Liu, Xiaoxin He, Miao Xiong +5
This paper proposes a simple yet effective jailbreak attack named FlipAttack against black-box LLMs. First, from the autoregressive nature, we reveal that LLMs tend to understand t…
Towards LifeSpan Cognitive Systems
Yu Wang, Chi Han, Tongtong Wu +9
Building a human-like system that continuously interacts with complex environments -- whether simulated digital worlds or human society -- presents several key challenges. Central…
ProCom: A Few-shot Targeted Community Detection Algorithm
Xixi Wu, Kaiyu Xiong, Yun Xiong +4
Targeted community detection aims to distinguish a particular type of community in the network. This is an important task with a lot of real-world applications, e.g., identifying f…
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering
Yuan Sui, Yufei He, Nian Liu +3
Large Language Models (LLMs) are often challenged by generating erroneous or hallucinated responses, especially in complex reasoning tasks. Leveraging Knowledge Graphs (KGs) as ext…
A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition
Nian Liu, Xiaoxin He, Thomas Laurent +3
Spectral graph convolution, an important tool of data filtering on graphs, relies on two essential decisions: selecting spectral bases for signal transformation and parameterizing…
Can we Soft Prompt LLMs for Graph Learning Tasks?
Zheyuan Liu, Xiaoxin He, Yijun Tian +1
Graph plays an important role in representing complex relationships in real-world applications such as social networks, biological data and citation networks. In recent years, Larg…
UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs
Yufei He, Yuan Sui, Xiaoxin He +1
Foundation models like ChatGPT and GPT-4 have revolutionized artificial intelligence, exhibiting remarkable abilities to generalize across a wide array of tasks and applications be…
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Xiaoxin He, Yijun Tian, Yifei Sun +5
Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface. In response to a user…