Time Sensitive Knowledge Editing through Efficient Finetuning

• URL: http://arxiv.org/abs/2406.04496v2
• Date: Tue, 23 Jul 2024 00:46:37 GMT
• Title: Time Sensitive Knowledge Editing through Efficient Finetuning
• Authors: Xiou Ge, Ali Mousavi, Edouard Grave, Armand Joulin, Kun Qian, Benjamin Han, Mostafa Arefiyan, Yunyao Li,
• Abstract summary: Large Language Models (LLMs) have demonstrated impressive capability in different tasks and are bringing transformative changes to many domains. Keeping the knowledge in LLMs up-to-date remains a challenge once pretraining is complete. Existing locate-and-edit knowledge editing (KE) method suffers from two limitations.
• Score: 35.79991957163508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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