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 dataset in which multi...