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cs.CL2026
RAdapter: A Routing and Rewriting Adapter for Efficient Hybrid RAG
Yucan Guo, Miao Su, Saiping Guan +6
Retrieval-Augmented Generation (RAG) has become a prevailing paradigm for enhancing Large Language Models (LLMs) with non-parametric knowledge. Vanilla RAG efficiently handles simp…
cs.CL2026
Event Ontology Expansion via LLM-Based Conceptualization
Weicheng Ren, Zixuan Li, Long Bai +3
Event ontology expansion aims to discover emerging event types from data and extend them to appropriate positions in the existing event ontology.. Existing methods typically cluste…
cs.CL2026
Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning
Naixin Zhai, Pengyang Shao, Binbin Zheng +4
Machine unlearning aims to forget sensitive knowledge from Large Language Models (LLMs) while maintaining general utility. However, existing approaches typically treat all tokens i…