The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability
Fine-tuning has been widely used to adapt large language models (LLMs) for domain-specific tasks. Parameter efficient fine-tuning (PEFT) methods such as low-rank adaptation (LoRA) are frequently used to reduce computational costs. PortLLM is a training-free and data-free scheme used to adapt LLMs after continual pretraining. Although the initial PortLLM results show that LoRA patches exhibit short-term temporal portability, the long-term performance of PortLLM across several updates of continual pretraining remains underexplored. Furthermore, the intriguing effectiveness of PortLLM is not well
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- FuzzyOverlapping authors or contributors · 62%zhayujie/CowAgent →
“Shared author/contributor keys: chan”
- LinkedLinked via arxiv author · 85%Abigail Woodring →
“The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability”
- LinkedLinked via arxiv author · 85%Adrian Chan →
“The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability”
- LinkedLinked via arxiv author · 85%Rana Muhammad Shahroz Khan →
“The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability”
- LinkedLinked via arxiv author · 85%Sukwon Yun →
“The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability”
- LinkedLinked via arxiv author · 85%Chau-Wai Wong →
“The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability”
- LinkedLinked via arxiv author · 85%Tianlong Chen →
“The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability”