AIR-Bench: A New Benchmark for Large Audio-Language Models

Table of Links

Abstract and 1. Introduction

2 Related Work

3 AIR-Bench and 3.1 Overview

3.2 Foundation Benchmark

3.3 Chat Benchmark

3.4 Evaluation Strategy

4 Experiments

4.1 Models

4.2 Main Results

4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias

5 Conclusion and References

A Detailed Results of Foundation Benchmark

5 Conclusion

In this paper, we present AIR-Bench, the first generative evaluation benchmark designed specifically for audio-language models. AIR-Bench comprises 19 audio tasks with over 19k single-choice questions in the foundation benchmark, as well as over 2k open-ended audio questions in the chat benchmark. Notably, the benchmark covers diverse audio types such as speech, natural sounds, and music. We also propose a novel audio mixing strategy to simulate audio from real-world scenarios more accurately. A standardized, objective, and reproducible evaluation framework is employed to automatically assess the quality of hypotheses generated by LALMs. We conduct a thorough evaluation of 9 prominent open-source LALMs. Additionally, we plan to launch and maintain a leaderboard that will serve as a platform for the community to access and compare model performance consistently over time.

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