Audio-Language Models Struggle with Negation, Study Finds

Jul 15, 2026

A new preprint demonstrates that audio-language embedding models, such as CLAP, fail to distinguish between affirmative and negated sound concepts, mapping both to nearly identical representations. The authors introduce NegEval-Audio, a framework that converts existing datasets into negation-aware tasks, revealing that model performance drops sharply when handling negation—even for recent multimodal LLM-based models. While a training-free steering method offers some improvement for one task, the overall limitation persists.

Why it matters: This work exposes a fundamental limitation in current audio-language models, showing that standard evaluation overlooks their inability to handle negation, which may impact real-world applications requiring nuanced understanding.

Full story at: arXiv Audio and Speech Processing ↗