Review the performance of audio-language models in AIR-bench's foundation benchmark, revealing strengths, weaknesses, and insights for future improvements.

This paper introduces AIR-Bench, the first generative evaluation benchmark for LALMs, featuring 19 audio tasks and an evaluation framework for assessing LALMs

Explore how GPT-4 aligns with human evaluations in assessing LALMs, revealing insights on performance consistency and the impact of positional bias.

Review LALM evaluation results, highlighting Qwen-Audio Turbo's performance in benchmarks and GPT-4's role in improving success rates across all models.

valuating Language-Aligned Audio Models (LALMs) like SpeechGPT and Qwen-AudioChat, using instruction-following benchmarks and latest model checkpoints.

This article explores the unified evaluation strategy for Language-Aligned Audio Models (LALMs), focusing on GPT-4's role in assessing audio-based tasks.

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