Models(LLMs)by integrating external knowledge to substantially improve accuracy and mitigate hallucinations.As a pivotal technology in the contemporary generative Artificial Intelligence(AI)landscape,RAG addresses fun...Models(LLMs)by integrating external knowledge to substantially improve accuracy and mitigate hallucinations.As a pivotal technology in the contemporary generative Artificial Intelligence(AI)landscape,RAG addresses fundamental challenges in knowledge-intensive tasks.This special issue serves as a dedicated platform to showcase these cutting-edge advancements.It features six rigorously peer-reviewed papers that present state-of-the-art research and applications in the rapidly evolving field of RAG.展开更多
In classic community detection, it is assumed that communities are exclusive, in the sense of either soft clustering or hard clustering. It has come to attention in the recent literature that many real-world problems ...In classic community detection, it is assumed that communities are exclusive, in the sense of either soft clustering or hard clustering. It has come to attention in the recent literature that many real-world problems violate this assumption, and thus overlapping community detection has become a hot research topic. The existing work on this topic uses either content or link information, but not both of them. In this paper, we deal with the issue of overlapping community detection by combining content and link information. We develop an effective solution called subgraph overlapping clustering (SOC) and evaluate this new approach in comparison with several peer methods in the literature that use either content or link information. The evaluations demonstrate the effectiveness and promise of SOC in dealing with large scale real datasets.展开更多
文摘Models(LLMs)by integrating external knowledge to substantially improve accuracy and mitigate hallucinations.As a pivotal technology in the contemporary generative Artificial Intelligence(AI)landscape,RAG addresses fundamental challenges in knowledge-intensive tasks.This special issue serves as a dedicated platform to showcase these cutting-edge advancements.It features six rigorously peer-reviewed papers that present state-of-the-art research and applications in the rapidly evolving field of RAG.
文摘In classic community detection, it is assumed that communities are exclusive, in the sense of either soft clustering or hard clustering. It has come to attention in the recent literature that many real-world problems violate this assumption, and thus overlapping community detection has become a hot research topic. The existing work on this topic uses either content or link information, but not both of them. In this paper, we deal with the issue of overlapping community detection by combining content and link information. We develop an effective solution called subgraph overlapping clustering (SOC) and evaluate this new approach in comparison with several peer methods in the literature that use either content or link information. The evaluations demonstrate the effectiveness and promise of SOC in dealing with large scale real datasets.
