most citedNoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models

2 citations · 2 across the 1 of their papers we have counts for

collaborators

2 papers

cs.CL2025

Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts

Jie Zou, Cheng Lin, Weikang Guo +4

Conversational recommender systems enable natural language conversations and thus lead to a more engaging and effective recommendation scenario. As the conversations for recommende…

cs.LG2024★ 2 cited

NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models

Cheng Lin, Lujun Li, Dezhi Li +3

In this paper, we introduce Nested Low-Rank Adaptation (NoRA), a novel approach to parameter-efficient fine-tuning that extends the capabilities of Low-Rank Adaptation (LoRA) techn…