1 citations · 2 across the 7 of their papers we have counts for
8 papers
MASBench: Benchmarking LLM-based Multi-Agent Collaboration under Partial Observability
Qizhi Chu, Zekai Yu, Sijie Wen +5
Large language models (LLMs) have progressively evolved into the core of autonomous agents. Building on this progress, LLM-based multi-agent systems (MAS) coordinate multiple agent…
Multimodal Generative Retrieval Model with Staged Pretraining for Food Delivery on Meituan
Boyu Chen, Tai Guo, Weiyu Cui +4
Multimodal retrieval models are becoming increasingly important in scenarios such as food delivery, where rich multimodal features can meet diverse user needs and enable precise re…
Masked Language Models are Good Heterogeneous Graph Generalizers
Jinyu Yang, Cheng Yang, Shanyuan Cui +5
Heterogeneous graph neural networks (HGNNs) excel at capturing structural and semantic information in heterogeneous graphs (HGs), while struggling to generalize across domains and…
CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models
Junze Chen, Xinjie Yang, Cheng Yang +4
Recommender systems (RSs) are designed to retrieve candidate items a user might be interested in from a large pool. A common approach is using graph neural networks (GNNs) to captu…
GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotations
Junze Chen, Cheng Yang, Shujie Li +4
Large language models (LLMs) have demonstrated their strong capabilities in various domains, and have been recently integrated for graph analysis as graph language models (GLMs). W…
GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaboration
Xin Li, Qizhi Chu, Yubin Chen +7
Graphs are widely used for modeling relational data in real-world scenarios, such as social networks and urban computing. Existing LLM-based graph analysis approaches either integr…
Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models
Xin Li, Weize Chen, Qizhi Chu +9
The need to analyze graphs is ubiquitous across various fields, from social networks to biological research and recommendation systems. Therefore, enabling the ability of large lan…
Enhancing DETRs Variants through Improved Content Query and Similar Query Aggregation
Yingying Zhang, Chuangji Shi, Xin Guo +4
The design of the query is crucial for the performance of DETR and its variants. Each query consists of two components: a content part and a positional one. Traditionally, the cont…