SAKE: Towards Editing Auditory Attribute Knowledge of Large
  Audio-Language Models
Authors: name: Chih-Kai Yang, name: Yen-Ting Piao, name: Tzu-Wen Hsu, name: Szu-Wei Fu, name: Zhehuai Chen, name: Ke-Han Lu, name: Sung-Feng Huang, name: Chao-Han Huck Yang, name: Yu-Chiang Frank Wang, name: Yun-Nung Chen, name: Hung-yi Lee
Organization: 台灣大學

Knowledge editing offers an efficient way to update model knowledge without
full retraining, but prior work has concentrated almost exclusively on textual
or visual modalities. We introduce SAKE, the first benchmark specifically
designed for editing auditory attribute knowledge in Large Audio-Language
Models (LALMs). Unlike factual updates, SAKE targets several abstract auditory
attributes, capturing knowledge types that go beyond conventional textual and
visual domains. We benchmark seven editing methods on two LALMs along four
dimensions: reliability, generality, audio/text locality, and portability.
Results highlight challenges such as preserving intra-attribute knowledge
unrelated to the edit, generalizing edits to multimodal reasoning, and
maintaining edits under sequential updates. SAKE provides a principled
framework to study how knowledge editing extends to the auditory modalities,
opening new directions for maintaining and adapting LALMs in more diverse
real-world scenarios.