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.
@article{arxiv.2403.14950,
title = {KnowLA: Enhancing Parameter-efficient Finetuning with Knowledgeable Adaptation},
author = {Xindi Luo and Zequn Sun and Jing Zhao and Zhe Zhao and Wei Hu},
journal= {arXiv preprint arXiv:2403.14950},
year = {2024}
}
Comments
Accepted in the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024)