KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable
  Adaptation
Xindi Luo Zequn Sun Jing Zhao Zhe Zhao Wei Hu
Parameter-efficient finetuning (PEFT) is a key technique for adapting large
language models (LLMs) to downstream tasks. In this paper, we study leveraging
knowledge graph embeddings to improve the effectiveness of PEFT. We propose a
knowledgeable adaptation method called KnowLA. It inserts an adaptation layer
into an LLM to integrate the embeddings of entities appearing in the input
text. The adaptation layer is trained in combination with LoRA on instruction
data. Experiments on six benchmarks with two popular LLMs and three knowledge
graphs demonstrate the effectiveness and robustness of KnowLA. We show that
\modelname can help activate the relevant parameterized knowledge in an LLM to
answer a question without changing its parameters or input prompts.