Reshaping Representation Space to Balance the Safety and Over-rejection in Large Audio Language Models

• URL: http://arxiv.org/abs/2505.19670v1
• Date: Mon, 26 May 2025 08:25:25 GMT
• Title: Reshaping Representation Space to Balance the Safety and Over-rejection in Large Audio Language Models
• Authors: Hao Yang, Lizhen Qu, Ehsan Shareghi, Gholamreza Haffari,
• Abstract summary: Large Audio Language Models (LALMs) have extended the capabilities of Large Language Models (LLMs)<n>Recent research has revealed that LALMs remain vulnerable to harmful queries due to insufficient safety-alignment.
• Score: 50.89022445197919
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Audio Language Models (LALMs) have extended the capabilities of Large Language Models (LLMs) by enabling audio-based human interactions. However, recent research has revealed that LALMs remain vulnerable to harmful queries due to insufficient safety-alignment. Despite advances in defence measures for text and vision LLMs, effective safety-alignment strategies and audio-safety dataset specifically targeting LALMs are notably absent. Meanwhile defence measures based on Supervised Fine-tuning (SFT) struggle to address safety improvement while avoiding over-rejection issues, significantly compromising helpfulness. In this work, we propose an unsupervised safety-fine-tuning strategy as remedy that reshapes model's representation space to enhance existing LALMs safety-alignment while balancing the risk of over-rejection. Our experiments, conducted across three generations of Qwen LALMs, demonstrate that our approach significantly improves LALMs safety under three modality input conditions (audio-text, text-only, and audio-only) while increasing over-rejection rate by only 0.88% on average. Warning: this paper contains harmful examples.

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