KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation

arXiv:2403.14950v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cs.cl cs.lg embeddings finetuning graph key knowledge knowledge graph language language models large language large language models layer llm llms paper peft study tasks type