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AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation Experts
May 2, 2024, 4:47 a.m. | Zefang Liu, Jiahua Luo
cs.CL updates on arXiv.org arxiv.org
Abstract: We introduce AdaMoLE, a novel method for fine-tuning large language models (LLMs) through an Adaptive Mixture of Low-Rank Adaptation (LoRA) Experts. Moving beyond conventional methods that employ a static top-k strategy for activating experts, AdaMoLE dynamically adjusts the activation threshold using a dedicated threshold network, adaptively responding to the varying complexities of different tasks. By replacing a single LoRA in a layer with multiple LoRA experts and integrating a gating function with the threshold mechanism, …
abstract arxiv beyond cs.cl experts fine-tuning language language models large language large language models llms lora low low-rank adaptation moving novel strategy threshold through type
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