SAKE: Towards Editing Auditory Attribute Knowledge of Large Audio-Language Models
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915863669506048 |
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| author | Yang, Chih-Kai Piao, Yen-Ting Hsu, Tzu-Wen Fu, Szu-Wei Chen, Zhehuai Lu, Ke-Han Huang, Sung-Feng Yang, Chao-Han Huck Wang, Yu-Chiang Frank Chen, Yun-Nung Lee, Hung-yi |
| author_facet | Yang, Chih-Kai Piao, Yen-Ting Hsu, Tzu-Wen Fu, Szu-Wei Chen, Zhehuai Lu, Ke-Han Huang, Sung-Feng Yang, Chao-Han Huck Wang, Yu-Chiang Frank Chen, Yun-Nung Lee, Hung-yi |
| contents | Knowledge editing enables targeted updates without retraining, but prior work focuses on textual or visual facts, leaving abstract auditory perceptual knowledge underexplored. We introduce SAKE, the first benchmark for editing perceptual auditory attribute knowledge in large audio-language models (LALMs), which requires modifying acoustic generalization rather than isolated facts. We evaluate eight diverse editing methods on three LALMs across reliability, generality, locality, and portability, under single and sequential edits. Results show that most methods enforce edits reliably but struggle with auditory generalization, intra-attribute locality, and multimodal knowledge propagation, and often exhibit forgetting or degeneration in sequential editing. Additionally, fine-tuning the modality connector emerges as a more robust and balanced baseline compared with directly editing the LLM backbones. SAKE reveals key limitations of current methods and provides a foundation for developing auditory-specific LALM editing techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16917 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SAKE: Towards Editing Auditory Attribute Knowledge of Large Audio-Language Models Yang, Chih-Kai Piao, Yen-Ting Hsu, Tzu-Wen Fu, Szu-Wei Chen, Zhehuai Lu, Ke-Han Huang, Sung-Feng Yang, Chao-Han Huck Wang, Yu-Chiang Frank Chen, Yun-Nung Lee, Hung-yi Sound Artificial Intelligence Computation and Language Audio and Speech Processing Knowledge editing enables targeted updates without retraining, but prior work focuses on textual or visual facts, leaving abstract auditory perceptual knowledge underexplored. We introduce SAKE, the first benchmark for editing perceptual auditory attribute knowledge in large audio-language models (LALMs), which requires modifying acoustic generalization rather than isolated facts. We evaluate eight diverse editing methods on three LALMs across reliability, generality, locality, and portability, under single and sequential edits. Results show that most methods enforce edits reliably but struggle with auditory generalization, intra-attribute locality, and multimodal knowledge propagation, and often exhibit forgetting or degeneration in sequential editing. Additionally, fine-tuning the modality connector emerges as a more robust and balanced baseline compared with directly editing the LLM backbones. SAKE reveals key limitations of current methods and provides a foundation for developing auditory-specific LALM editing techniques. |
| title | SAKE: Towards Editing Auditory Attribute Knowledge of Large Audio-Language Models |
| topic | Sound Artificial Intelligence Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2510.16917 |