This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all LM parameters, we only tune a few new parameters while keeping the original LM parameters fixed. We establish this via reformulating KG completion as a "fill-in-the-blank" task, and introducing a parameter-lite encoder on top of the original LMs. We show that, by tuning far fewer parameters than finetuning, LMs transfer non-trivially to most tasks and reach competitiveness with prior state-of-the-art approaches. For instance, we outperform the fully finetuning approaches on a KG completion benchmark by tuning only 1% of the parameters. The code and datasets are available at \url{https://github.com/yuanyehome/PALT}.
@article{arxiv.2210.13715,
title = {PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion},
author = {Jianhao Shen and Chenguang Wang and Ye Yuan and Jiawei Han and Heng Ji and Koushik Sen and Ming Zhang and Dawn Song},
journal= {arXiv preprint arXiv:2210.13715},
year = {2022}
}