Retrieval-Augmented Generation (RAG) relies critically on the effectiveness of its retrieval component. Dense retrieval with Approximate Nearest Neighbor (ANN) search is efficient but relies on symmetric similarity metrics that can limit the modeling of directional relevance. Generative retrieval addresses this limitat...
Yifei Zhang, Hao Zhu, Haoran Shi et al.· Proceedings of the 32nd ACM...· 0 citations
A temporal graph is an undirected graph where each edge is associated with a timestamp representing the interaction time. As a fundamental problem in graph analysis, community search (CS) in temporal graphs has received tremendous research attention. Existing CS works on temporal graphs typically focus on identifying...
Yue Zhang, Ying-Li Zhou, Fang-Yuan Zhang et al.· Proceedings of the ACM on Ma...· 0 citations
This work proposes eXtreme Gradient Boosting LoRA (XGBLoRA), a novel framework grounded in gradient boosting theory that provides theoretical analysis establishing convergence guarantees and expressiveness bounds, which formally justify why weaker (lower-rank) adapters, when properly combined, can match or exceed the p...
Yifei Zhang, Hao Zhu, Haoran Shi et al.· Proceedings of the 32nd ACM...· 0 citations
Fine-tuning Large Language Models (LLMs) has become a crucial technique for adapting pre-trained models to downstream tasks. However, the enormous size of LLMs poses significant challenges in terms of computational complexity and resource requirements. Low-Rank Adaptation (LoRA) has emerged as a promising solution, yet...
Yifei Zhang, Hao Zhu, Haoran Shi et al.· Proceedings of the 32nd ACM...· 0 citations
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