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Uncovering Ordinal-Matching Bias in Audio-Visual LLMs
This work aims to improve how audio-visual large language models (AVLLMs) associate speech with the correct visible speaker in multi-speaker scenes. We find that current AVLLMs frequently fail at this task, and analyze the nature of these failures. To this end, we construct a synthetic diagnostic da
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