  Large Language Models (LLMs) have demonstrated impressive capability in
different tasks and are bringing transformative changes to many domains.
However, keeping the knowledge in LLMs up-to-date remains a challenge once
pretraining is complete. It is thus essential to design effective methods to
both update obsolete knowledge and induce new knowledge into LLMs. Existing
locate-and-edit knowledge editing (KE) method suffers from two limitations.
First, the post-edit LLMs by such methods generally have poor capability in
answering complex queries that require multi-hop reasoning. Second, the long
run-time of such locate-and-edit methods to perform knowledge edits make it
infeasible for large scale KE in practice. In this paper, we explore
Parameter-Efficient Fine-Tuning (PEFT) techniques as an alternative for KE. We
curate a more comprehensive temporal KE dataset with both knowledge update and
knowledge injection examples for KE performance benchmarking. We further probe
the effect of fine-tuning on a range of layers in an LLM for the multi-hop QA
task. We find that PEFT performs better than locate-and-edit techniques for
time-sensitive knowledge edits.
