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20232026
most citedAre We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review

9 citations · 18 across the 30 of their papers we have counts for

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5 papers · 1 filter

cs.CL2024★ 9 cited

Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review

Rui Ye, Xianghe Pang, Jingyi Chai +6

Scholarly peer review is a cornerstone of scientific advancement, but the system is under strain due to increasing manuscript submissions and the labor-intensive nature of the proc…

cs.CR2024★ 2 cited

SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents

Sheng Yin, Xianghe Pang, Yuanzhuo Ding +7

With the integration of large language models (LLMs), embodied agents have strong capabilities to understand and plan complicated natural language instructions. However, a foreseea…

cs.LG2024

Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks

Peizhi Niu, Chao Pan, Siheng Chen +1

Graph neural networks (GNNs) have become instrumental in diverse real-world applications, offering powerful graph learning capabilities for tasks such as social networks and medica…

cs.LG2024★ 1 cited

Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning

Shuo Tang, Rui Ye, Chenxin Xu +3

Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server, with each agent solving varied…

cs.LG2024★ 1 cited

Training-Free Message Passing for Learning on Hypergraphs

Bohan Tang, Zexi Liu, Keyue Jiang +2

Hypergraphs are crucial for modelling higher-order interactions in real-world data. Hypergraph neural networks (HNNs) effectively utilise these structures by message passing to gen…