arXiv:2509.14659v3 Announce Type: replace-cross Abstract: Current audio captioning relies on supervised learning with paired audio-caption data, which is costly to curate and may not reflect human preferences in real-world scenarios. To address this, we propose a preference-aligned audio captioning framework based on Reinforcement Learning from Human Feedback (RLHF). To capture nuanced preferences, we train a Contrastive Language-Audio Pretraining (CLAP) based reward model using human-labeled pairwise preference data. This reward model is integrated into an RL framework to fine-tune any baseli
The increasing sophistication of AI models and the rising cost and limitations of curated supervised datasets are driving innovation towards more efficient training methodologies.
This research potentially lowers the barrier to creating high-quality audio captioning systems by reducing reliance on expensive hand-labeled data, making advanced AI capabilities more accessible.
The development of audio captioning could accelerate through preference-aligned methods, moving from costly supervised learning to more scalable reinforcement learning from human feedback.
- · AI developers
- · Audio content platforms
- · Speech technology companies
More accurate and nuanced audio captioning systems become available.
This approach could be generalized to other modalities, reducing data annotation needs across various AI applications.
Enhanced AI understanding of auditory data could lead to new forms of human-computer interaction and content analysis.
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Read at arXiv cs.LG