REVE: Efficient Hallucination Correction for Large Audio-Language Models via Reused Encoder States

Abstract:Large audio-language models may mention acoustic events that are absent from the input. A separate audio event detector can verify these mentions, but doing so requires a second audio encoder and a separate forward pass. We propose Reused Encoder States for Verifying Events (REVE), a lightweight method that uses states already computed by the target model. One readout summarizes class scores across audio frames, while another uses pooled states from four consecutive frame intervals. Class-aware score fusion combines their outputs to verify generated event mentions without encoding the audio again. On AudioSet, REVE removes 92.9% of label-unsupported mentions under a faithful-mention recall constraint. With fewer added parameters and no second audio-encoding pass, REVE achieves a reduction comparable to those of CED-Tiny and CED-Base. Its complete verification latency is about 1/18 of the CED-Base path. Results on controlled DESED mixtures and different target-model architectures further confirm the effectiveness of encoder-state reuse.

Sound (cs.SD); Artificial Intelligence (cs.AI)

arXiv:2609.26028 [cs.SD]

(or arXiv:2609.26028v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2609.26028 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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