SAKE: Towards Editing Auditory Attribute Knowledge of Large
  Audio-Language Models
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.
68f78e8ea7b9b96b2c13edc4
Chih-Kai Yang
claimed_verified
2025-10-21T14:00:14.845Z
646fa3016441111304fec68d
/avatars/923629340f3785ae8c6e52cf3674d5c2.svg
Chih-Kai Yang
user
zenyn
68f78e8ea7b9b96b2c13edc5
Yen-Ting Piao
claimed_verified
2025-10-24T16:13:54.361Z
6623864025bb4e9dc1f09695
/avatars/d37dc450d48cd1e9be36608c0ece0bcd.svg
Yenting
user
yenting-biao
68f78e8ea7b9b96b2c13edc6
Tzu-Wen Hsu
68f78e8ea7b9b96b2c13edc7
Szu-Wei Fu
68f78e8ea7b9b96b2c13edc8
Zhehuai Chen
68f78e8ea7b9b96b2c13edc9
Ke-Han Lu
68f78e8ea7b9b96b2c13edca
Sung-Feng Huang
68f78e8ea7b9b96b2c13edcb
Chao-Han Huck Yang
68f78e8ea7b9b96b2c13edcc
Yu-Chiang Frank Wang
68f78e8ea7b9b96b2c13edcd
Yun-Nung Chen
68f78e8ea7b9b96b2c13edce
Hung-yi Lee
SAKE: Towards Editing Auditory Attribute Knowledge of Large
  Audio-Language Models
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.
SAKE is a benchmark for editing auditory attribute knowledge in Large Audio-Language Models, addressing challenges in reliability, generality, locality, and portability.
Large Audio-Language Models LALMs knowledge editing auditory attribute knowledge benchmark reliability generality audio/text locality portability intra-attribute knowledge multimodal reasoning sequential updates