Title: SAKE: Towards Editing Auditory Attribute Knowledge of Large Audio-Language Models
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Large audio-language models, auditory attribute, knowledge editing

Abstract: 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.

TL;DR: The first benchmark comprehensively evaluates the knowledge editing of auditory attribute knowledge in large audio-language models.