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
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
An LLM reasoning framework with hierarchical relational retrieval for large-scale knowledge updating, named G-HiRel, which achieves superiority in terms of accuracy and interpretability and handles the knowledge inconsistency between the KG and LLM to obtain entity independence.
Yudai Pan, Jiajie Hong, Tianzhe Zhao et al.· Annual Meeting of the Associ...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
