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

• URL: http://arxiv.org/abs/2607.12290v1
• Date: Tue, 14 Jul 2026 02:55:00 GMT
• Title: The Sound of Absence: Audio-Language Embedding Models Struggle with Negation
• Abstract summary: We show that embedding models fail to encode negated sound concepts.<n>We introduce NegEval-Audio, a framework that converts datasets into two negation-aware tasks.
• Score: 58.04745279785462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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