KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models
Authors: name: Fan Wang, name: Juyong Jiang, name: Chansung Park, name: Sunghun Kim, name: Jing Tang

The increasing sizes of large language models (LLMs) result in significant
computational overhead and memory usage when adapting these models to specific
tasks or domains. Various parameter-efficient fine-tuning (PEFT) methods have
been devised to mitigate these challenges by training a small set of parameters
for the task-specific updates of the model weights. Among PEFT methods, LoRA
stands out for its simplicity and efficiency, inspiring the development of a
series of variants. However, LoRA and its successors disregard the knowledge
that is noisy or irrelevant to the targeted task, detrimentally impacting model
performance and leading to suboptimality. To address this limitation, we
introduce Knowledge-aware Singular-value Adaptation (KaSA), a PEFT method that
leverages singular value decomposition (SVD) with knowledge-aware singular
values to dynamically activate knowledge based on its relevance to the task at
hand. We conduct extensive experiments across a range of LLMs on tasks spanning
natural language understanding (NLU), generation (NLG), instruction following,
and commonsense reasoning. The experimental results demonstrate that KaSA
consistently outperforms FFT and 14 popular PEFT baselines across 16 benchmarks
and 4 synthetic datasets, underscoring our method's efficacy and adaptability.
The source code of our method is available at
https://github.com/juyongjiang/KaSA.