40 citations · 89 across the 42 of their papers we have counts for
43 papers
The Past Is Prologue: A Plug-in Controller for Selective Updates in Sequentially Evolving LLM Memory
Zihan Chen, Songwei Dong, Chengshuai Shi +4
Sequentially evolving LLM memory enables agents to reuse past experience, but existing systems usually deploy each locally generated memory update without checking whether it impro…
Thousand-GPU Large-Scale Training and Optimization Recipe for AI-Native Cloud Embodied Intelligence Infrastructure
Yongjian Guo, Yunxuan Ma, Haoran Sun +22
Embodied intelligence is a key step towards Artificial General Intelligence (AGI), yet its development faces multiple challenges including data, frameworks, infrastructure, and eva…
BrainTAP: Brain Disorder Prediction with Adaptive Distill and Selective Prior Integration
Zhenyu Lei, Aiying Zhang, Song Wang +2
Predicting clinical outcomes from brain networks in large-scale neuroimaging cohorts such as the Adolescent Brain Cognitive Development (ABCD) study requires effectively integratin…
From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning
Zihan Chen, Song Wang, Xingbo Fu +4
The capability of in-context learning (ICL) enables large language models (LLMs) to perform novel tasks without parameter updates by conditioning on a few input-output examples. Ho…
Interpretable Neuropsychiatric Diagnosis via Concept-Guided Graph Neural Networks
Song Wang, Zhenyu Lei, Zhen Tan +4
Nearly one in five adolescents currently live with a diagnosed mental or behavioral health condition, such as anxiety, depression, or conduct disorder, underscoring the urgency of…
Learning from Diverse Reasoning Paths with Routing and Collaboration
Zhenyu Lei, Zhen Tan, Song Wang +4
Advances in large language models (LLMs) significantly enhance reasoning capabilities but their deployment is restricted in resource-constrained scenarios. Knowledge distillation a…
MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning
Zihan Chen, Song Wang, Zhen Tan +2
In-Context Learning (ICL) empowers Large Language Models (LLMs) to tackle diverse tasks by incorporating multiple input-output examples, known as demonstrations, into the input of…
Are We Merely Justifying Results ex Post Facto? Quantifying Explanatory Inversion in Post-Hoc Model Explanations
Zhen Tan, Song Wang, Yifan Li +4
Post-hoc explanation methods provide interpretation by attributing predictions to input features. Natural explanations are expected to interpret how the inputs lead to the predicti…
A Survey of Scaling in Large Language Model Reasoning
Zihan Chen, Song Wang, Zhen Tan +6
The rapid advancements in large Language models (LLMs) have significantly enhanced their reasoning capabilities, driven by various strategies such as multi-agent collaboration. How…
Question-Aware Knowledge Graph Prompting for Enhancing Large Language Models
Haochen Liu, Song Wang, Chen Chen +1
Large Language Models (LLMs) often struggle with tasks requiring external knowledge, such as knowledge-intensive Multiple Choice Question Answering (MCQA). Integrating Knowledge Gr…
Reasoning of Large Language Models over Knowledge Graphs with Super-Relations
Song Wang, Junhong Lin, Xiaojie Guo +3
While large language models (LLMs) have made significant progress in processing and reasoning over knowledge graphs, current methods suffer from a high non-retrieval rate. This lim…
Generative Risk Minimization for Out-of-Distribution Generalization on Graphs
Song Wang, Zhen Tan, Yaochen Zhu +2
Out-of-distribution (OOD) generalization on graphs aims at dealing with scenarios where the test graph distribution differs from the training graph distributions. Compared to i.i.d…
BrainMAP: Learning Multiple Activation Pathways in Brain Networks
Song Wang, Zhenyu Lei, Zhen Tan +8
Functional Magnetic Resonance Image (fMRI) is commonly employed to study human brain activity, since it offers insight into the relationship between functional fluctuations and hum…
Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization
Song Wang, Xiaodong Yang, Rashidul Islam +4
To deal with distribution shifts in graph data, various graph out-of-distribution (OOD) generalization techniques have been recently proposed. These methods often employ a two-step…
KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models
Zaiyi Zheng, Yushun Dong, Song Wang +3
Large Language Models (LLMs) have shown impressive performance in various tasks, including knowledge graph completion (KGC). However, current studies mostly apply LLMs to classific…
Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective
Yushun Dong, Patrick Soga, Yinhan He +2
Graph Neural Networks (GNNs) have achieved remarkable success in various graph-based learning tasks. While their performance is often attributed to the powerful neighborhood aggreg…
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
Xingbo Fu, Zihan Chen, Yinhan He +4
Federated Graph Learning (FGL) enables multiple clients to jointly train powerful graph learning models, e.g., Graph Neural Networks (GNNs), without sharing their local graph data…
Federated Graph Learning with Graphless Clients
Xingbo Fu, Song Wang, Yushun Dong +3
Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs), for multiple clients, each with its own graph data. Existing m…
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
Kexin Zhang, Shuhan Liu, Song Wang +6
Distribution shifts on graphs -- the discrepancies in data distribution between training and employing a graph machine learning model -- are ubiquitous and often unavoidable in rea…
Understanding and Modeling Job Marketplace with Pretrained Language Models
Yaochen Zhu, Liang Wu, Binchi Zhang +5
Job marketplace is a heterogeneous graph composed of interactions among members (job-seekers), companies, and jobs. Understanding and modeling job marketplace can benefit both job…
A Benchmark for Fairness-Aware Graph Learning
Yushun Dong, Song Wang, Zhenyu Lei +4
Fairness-aware graph learning has gained increasing attention in recent years. Nevertheless, there lacks a comprehensive benchmark to evaluate and compare different fairness-aware…
CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models
Song Wang, Peng Wang, Tong Zhou +3
As Large Language Models (LLMs) are increasingly deployed to handle various natural language processing (NLP) tasks, concerns regarding the potential negative societal impacts of L…
"Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models
Zhen Tan, Chengshuai Zhao, Raha Moraffah +5
Retrieval-Augmented Generative (RAG) models enhance Large Language Models (LLMs) by integrating external knowledge bases, improving their performance in applications like fact-chec…
Few-shot Knowledge Graph Relational Reasoning via Subgraph Adaptation
Haochen Liu, Song Wang, Chen Chen +1
Few-shot Knowledge Graph (KG) Relational Reasoning aims to predict unseen triplets (i.e., query triplets) for rare relations in KGs, given only several triplets of these relations…