Introduces the MUSA benchmark and evaluates LALMs showing that strong single-speaker performance fails to ensure robust selective attention under multilingual interference, with errors from source confusion and unresolved attribution after separation.
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2 Pith papers cite this work. Polarity classification is still indexing.
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The survey introduces a four-category taxonomy for LALM evaluations and reviews benchmarks across general auditory processing, knowledge reasoning, dialogue, and fairness-safety.
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Can Large Audio Language Models Ignore Multilingual Distractors? An Evaluation of Their Selective Auditory Attention Capabilities
Introduces the MUSA benchmark and evaluates LALMs showing that strong single-speaker performance fails to ensure robust selective attention under multilingual interference, with errors from source confusion and unresolved attribution after separation.
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Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey
The survey introduces a four-category taxonomy for LALM evaluations and reviews benchmarks across general auditory processing, knowledge reasoning, dialogue, and fairness-safety.