The Sound of Absence: Audio-Language Embedding Models Struggle with Negation

Audio-language embedding models such as CLAP are widely evaluated on matching present sound events, but rarely on negation.

We show this affirmation-only evaluation hides a key limitation: these models fail to encode negated sound concepts, mapping affirmative and negated captions to nearly identical representations.

To expose this blind spot, we introduce NegEval-Audio, a framework that converts existing datasets into two negation-aware tasks, Retrieval-Neg and Multiple-Choice Negation (MCQ-Neg), to probe whether models distinguish present from absent events.

On AudioCaps and Clotho, performance degrades sharply under negation, with negation-type MCQ accuracy falling far below chance, and the failure persists even for a recent multimodal LLM-based embedding model.

While a training-free steering method improves MCQ-Neg, it yields marginal gains for Retrieval-Neg.

This indicates that affirmation bias is a fundamental flaw in the representation geometry, necessitating explicit negation-aware training objectives.