87 citations · 246 across the 12 of their papers we have counts for
12 papers
QLASS: Boosting Language Agent Inference via Q-Guided Stepwise Search
Zongyu Lin, Yao Tang, Xingcheng Yao +4
Language agents have become a promising solution to complex interactive tasks. One of the key ingredients to the success of language agents is the reward model on the trajectory of…
AVIS: Autonomous Visual Information Seeking with Large Language Model Agent
Ziniu Hu, Ahmet Iscen, Chen Sun +5
In this paper, we propose an autonomous information seeking visual question answering framework, AVIS. Our method leverages a Large Language Model (LLM) to dynamically strategize t…
Dissimilar Nodes Improve Graph Active Learning
Zhicheng Ren, Yifu Yuan, Yuxin Wu +3
Training labels for graph embedding algorithms could be costly to obtain in many practical scenarios. Active learning (AL) algorithms are very helpful to obtain the most useful lab…
REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory
Ziniu Hu, Ahmet Iscen, Chen Sun +6
In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it…
Empowering Language Models with Knowledge Graph Reasoning for Question Answering
Ziniu Hu, Yichong Xu, Wenhao Yu +5
Answering open-domain questions requires world knowledge about in-context entities. As pre-trained Language Models (LMs) lack the power to store all required knowledge, external kn…
Improving Multi-Task Generalization via Regularizing Spurious Correlation
Ziniu Hu, Zhe Zhao, Xinyang Yi +4
Multi-Task Learning (MTL) is a powerful learning paradigm to improve generalization performance via knowledge sharing. However, existing studies find that MTL could sometimes hurt…
Relation-Guided Pre-Training for Open-Domain Question Answering
Ziniu Hu, Yizhou Sun, Kai-Wei Chang
Answering complex open-domain questions requires understanding the latent relations between involving entities. However, we found that the existing QA datasets are extremely imbala…
Fuzzy Logic Based Logical Query Answering on Knowledge Graphs
Xuelu Chen, Ziniu Hu, Yizhou Sun
Answering complex First-Order Logical (FOL) queries on large-scale incomplete knowledge graphs (KGs) is an important yet challenging task. Recent advances embed logical queries and…
GPT-GNN: Generative Pre-Training of Graph Neural Networks
Ziniu Hu, Yuxiao Dong, Kuansan Wang +2
Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data. However, training GNNs usually requires abundant task-specific labeled data, w…
Heterogeneous Graph Transformer
Ziniu Hu, Yuxiao Dong, Kuansan Wang +1
Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all…
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Difan Zou, Ziniu Hu, Yewen Wang +3
Graph convolutional networks (GCNs) have recently received wide attentions, due to their successful applications in different graph tasks and different domains. Training GCNs for a…
Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
Ziniu Hu, Changjun Fan, Ting Chen +2
Graph neural networks (GNNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GNN model requires a large collection of label…