Singular Vector

Singular vectors, derived from singular value decomposition (SVD) of matrices, are fundamental components in various machine learning applications, particularly in efficiently adapting large language models (LLMs). Current research focuses on leveraging singular vectors for parameter-efficient fine-tuning (PEFT) of LLMs, employing techniques that selectively update subsets of singular vectors or their associated singular values to achieve performance comparable to full fine-tuning with significantly fewer parameters. These methods offer substantial improvements in computational efficiency and memory usage for training and deploying LLMs, impacting both research and practical applications in natural language processing and beyond.