AOR-Bench: Do Large Audio Language Models Over-Refuse Pseudo-Harmful Queries?
Large Audio Language Models (LALMs) have demonstrated strong performance across a wide range of audio tasks. As they are increasingly deployed in real-world applications, ensuring their safety alignment has become more important. Although refusal mechanisms serve as a key safeguard by preventing LALMs from responding to harmful requests, they can also lead to over-refusal, where models incorrectly reject benign queries. This issue is especially challenging in the audio domain because speech that appears harmful in isolation may become benign when interpreted together with the surrounding acoustic context, such as background sounds. To study this problem, we introduce AOR-Bench (Audio Over...
| Authors | Jiaxi Yang, Chaewan Chun, Jason Lucas et al. |
|---|---|
| Primary category | cs.SD |
| Published | 19 June 2026 |
| Last updated at source | 26 August 2026 |
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