Improving Text-to-Audio Instruction Following via Fine-Grained Feedback from Audio-Aware Large Language Models

Jul 16, 2026

Researchers propose a framework that leverages audio-aware large language models (ALLMs) to provide fine-grained feedback on the presence and temporal order of events in generated audio. This feedback is used for direct preference optimization, leading to improved instruction-following accuracy in text-to-audio models. The work also introduces S3Bench, a new benchmark for evaluating multi-event temporal instruction following. Experiments demonstrate that the method enhances event completeness and temporal ordering without compromising audio quality.

Why it matters: The approach addresses a major challenge in text-to-audio generation by enabling models to better follow complex, temporally-ordered instructions through scalable supervision.

Full story at: arXiv Audio and Speech Processing ↗