تقرير
Time Sensitive Knowledge Editing through Efficient Finetuning
| العنوان: | Time Sensitive Knowledge Editing through Efficient Finetuning |
|---|---|
| المؤلفون: | Ge, Xiou, Mousavi, Ali, Grave, Edouard, Joulin, Armand, Qian, Kun, Han, Benjamin, Arefiyan, Mostafa, Li, Yunyao |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | 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. Comment: ACL 2024 main |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.04496 |
| رقم الأكسشن: | edsarx.2406.04496 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |