arXiv Machine Learning June 16, 2026 By Hyebin Cho, Jaehyuk Jang, Changick Kim, Joon Son Chung

Acoustic Prompting via Stage-wise Modulation for Few-Shot Learning in Audio Language Models

Read the original on arXiv Machine Learning →

arXiv:2606. 15751v1 Announce Type: cross Abstract: Audio-Language Models (ALMs) have shown remarkable success in zero-shot audio classification by aligning audio waveforms with text.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv:2606. 31587v1 Announce Type: cross Abstract: Audio-Language Models (ALMs) achieve strong zero-shot performance by aligning audio with textual class descriptions.

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llms nlp fine-tuning

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llms reinforcement-learning multimodal

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ZEBRA: Zero-Shot Entropy-Regularized Prompt Learning for Base-to-Novel Generalization in Audio-Language Models

Audio-FLAN: An Instruction-Following Dataset for Unified Audio Understanding and Generation of Speech, Music, and Sound

FSA-GRPO: Teaching Auditory LLMs to Use Few-shot Demonstrations

SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification

Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation

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