A contrastive-style adapter trained on LLM-generated positive and negative audio descriptions improves audio hallucination accuracy to 77.5 percent and audio question answering to 84.3 percent, without changing the frozen language model.
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Teaching Audio-Aware Large Language Models What Does Not Hear: Mitigating Hallucinations through Synthesized Negative Samples
A contrastive-style adapter trained on LLM-generated positive and negative audio descriptions improves audio hallucination accuracy to 77.5 percent and audio question answering to 84.3 percent, without changing the frozen language model.