Audio Captioning (AC) plays a pivotal role in enhancing audio-text cross-modal understanding during the pretraining and finetuning of Multimodal LLMs (MLLMs). To strengthen this alignment, recent works propose Audio Difference Captioning (ADC), which takes multiple audio inputs and encourages the model to describe their differences, thereby promoting fine-grained discrimination. However, despite its effectiveness, ADC introduces a semantic gap between input audios-often rich in diverse events-and the brief, difference-focused short caption. This deviation from AC-style task causes a mismatch with the pretraining objective, leading to catastrophic forgetting. To address this, we propose Audio Commonality Captioning (ACC), a comparably challenging but gentler alternative that guides the model to capture shared semantics across audio clips rather than detailed differences. Experiments show that ACC not only improves audio-text understanding on captioning benchmarks but also better preserves general capabilities across diverse speech and music tasks, confirming its ability to enable more robust cross-modal understanding and achieve a better balance between generalization and task-specific performance in MLLMs.

翻译：音频描述（AC）在多模态大语言模型（MLLMs）的预训练与微调过程中，对于增强音频-文本跨模态理解起着关键作用。为强化这种对齐，近期研究提出了音频差异描述（ADC），该方法接收多个音频输入并促使模型描述其差异，从而促进细粒度判别。然而，尽管ADC有效，它却在输入音频（通常包含丰富多样的事件）与简短、聚焦差异的描述之间引入了语义鸿沟。这种与AC式任务的偏离导致了与预训练目标的不匹配，进而引发灾难性遗忘。为解决这一问题，我们提出了音频共性描述（ACC），这是一种具有相当挑战性但更为温和的替代方案，它引导模型捕捉音频片段间的共享语义，而非详细差异。实验表明，ACC不仅提升了在描述基准上的音频-文本理解能力，还能在多样化的语音和音乐任务中更好地保持通用能力，证实了其能够实现更稳健的跨模态理解，并在MLLMs的泛化能力与任务特定性能之间达成更优平衡。