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pro vyhledávání: '"YU, Shuo"'
This paper presents the solution of our team APEX in the Meta KDD CUP 2024: CRAG Comprehensive RAG Benchmark Challenge. The CRAG benchmark addresses the limitations of existing QA benchmarks in evaluating the diverse and dynamic challenges faced by R
Externí odkaz:
http://arxiv.org/abs/2409.15337
Autor:
Yu, Shuo, Cheng, Mingyue, Yang, Jiqian, Ouyang, Jie, Luo, Yucong, Lei, Chenyi, Liu, Qi, Chen, Enhong
Retrieval-augmented generation (RAG) is increasingly recognized as an effective approach for mitigating the hallucination of large language models (LLMs) through the integration of external knowledge. While numerous efforts, most studies focus on a s
Externí odkaz:
http://arxiv.org/abs/2409.13694
Autor:
Shehzad, Ahsan, Xia, Feng, Abid, Shagufta, Peng, Ciyuan, Yu, Shuo, Zhang, Dongyu, Verspoor, Karin
Graph transformers are a recent advancement in machine learning, offering a new class of neural network models for graph-structured data. The synergy between transformers and graph learning demonstrates strong performance and versatility across vario
Externí odkaz:
http://arxiv.org/abs/2407.09777
The training of large models, involving fine-tuning, faces the scarcity of high-quality data. Compared to the solutions based on centralized data centers, updating large models in the Internet of Things (IoT) faces challenges in coordinating knowledg
Externí odkaz:
http://arxiv.org/abs/2407.05268
Quantum conferencing enables multiple nodes within a quantum network to share a secure group key for private message broadcasting. The key rate, however, is limited by the repeaterless capacity to distribute multiparticle entangled states across the
Externí odkaz:
http://arxiv.org/abs/2407.00897
Quantum conference key agreement facilitates secure communication among multiple parties through multipartite entanglement and is anticipated to be an important cryptographic primitive for future quantum networks. However, the experimental complexity
Externí odkaz:
http://arxiv.org/abs/2406.15853
Autor:
Jing, Erkang, Liu, Yezheng, Chai, Yidong, Yu, Shuo, Liu, Longshun, Jiang, Yuanchun, Wang, Yang
Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users' preferences for music moods. However, existing
Externí odkaz:
http://arxiv.org/abs/2406.14090
COVID-19 as a global pandemic causes a massive disruption to social stability that threatens human life and the economy. Policymakers and all elements of society must deliver measurable actions based on the pandemic's severity to minimize the detrime
Externí odkaz:
http://arxiv.org/abs/2406.06618
Scientific collaboration is a significant behavior in knowledge creation and idea exchange. To tackle large and complex research questions, a trend of team formation has been observed in recent decades. In this study, we focus on recognizing collabor
Externí odkaz:
http://arxiv.org/abs/2406.06617
Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems
Federated recommender systems have been crucially enhanced through data sharing and continuous model updates, attributed to the pervasive connectivity and distributed computing capabilities of Internet of Things (IoT) devices. Given the sensitivity o
Externí odkaz:
http://arxiv.org/abs/2406.04702