AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension

Understand

Recently, instruction-following audio-language models have received broad attention for human-audio interaction.

• However, the absence of benchmarks capable of evaluating audio-centric interaction capabilities has impeded advancements in this field.
• Previous models primarily focus on assessing different fundamental tasks, such as Automatic Speech Recognition (ASR), and lack an assessment of the open-ended generative capabilities centered around audio.
• Thus, it is challenging to track the progression in the Large Audio-Language Models (LALMs) domain and to provide guidance for future improvement.