3 papers
cs.CL2025
Learning Wisdom from Errors: Promoting LLM's Continual Relation Learning through Exploiting Error Cases
Shaozhe Yin, Jinyu Guo, Kai Shuang +2
Continual Relation Extraction (CRE) aims to continually learn new emerging relations while avoiding catastrophic forgetting. Existing CRE methods mainly use memory replay and contr…
cs.IR2025
HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation
Jinyu Guo, Xunlei Chen, Qiyang Xia +5
Retrieval-Augmented Generation (RAG) encounters efficiency challenges when scaling to massive knowledge bases while preserving contextual relevance. We propose Hash-RAG, a framewor…
cs.AI2025
Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps
Jie Ou, Jinyu Guo, Shuaihong Jiang +4
Retrieval-augmented generation (RAG) has emerged as a pivotal method for expanding the knowledge of large language models. To handle complex queries more effectively, researchers d…