arXiv AI June 3, 2026 By Haolong Zheng, Siyin Wang, Xulin Fan, Zengrui Jin, Mark Hasegawa-Johnson

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

arXiv:2606. 02615v1 Announce Type: cross Abstract: Few-shot prompting provides an effective way to adapt auditory large language models to low-resource tasks such as children's speech recognition.

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

arXiv AI

Aug 10

MetaSICL: Globalizing Auditory LLMs for Underserved Speakers and Languages via Meta Speech In-Context Learning

arXiv:2601. 18904v3 Announce Type: replace-cross Abstract: Generative AI for speech and audio is increasingly expected to serve users across languages, cultures, and communities, yet current auditory Large Language Models (LLMs) are still largely trained and evaluated on high-resource data.

By Haolong Zheng, Siyin Wang, Zengrui Jin, Mark Hasegawa-Johnson

llms reinforcement-learning fine-tuning multimodal

arXiv Machine Learning

Jun 16

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

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.

By Hyebin Cho, Jaehyuk Jang, Changick Kim, Joon Son Chung

llms

arXiv AI

Jun 9

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

arXiv:2502. 16584v2 Announce Type: replace-cross Abstract: Recent advancements in audio tokenization have significantly enhanced the integration of audio capabilities into large language models (LLMs).

By Liumeng Xue, Ziya Zhou, Jiahao Pan, Zixuan Li, Shuai Fan, Yinghao Ma, Sitong Cheng, Dongchao Yang, Haohan Guo, Yujia Xiao, Xinsheng Wang, Zixuan Shen, Chuanbo Zhu, Xinshen Zhang, Tianchi Liu, Ruibin Yuan, Zeyue Tian, Haohe Liu, Xingjian Du, Emmanouil Benetos, Ge Zhang, Yike Guo, Wei Xue

llms nlp fine-tuning

arXiv AI

Jun 26

VoiceTTA: Enhancing Zero-Shot Text-to-Speech via Reinforcement Learning-Based Test-Time Adaptation

arXiv:2606. 26534v1 Announce Type: cross Abstract: Recently, zero-shot text-to-speech (TTS) has enabled high-fidelity and expressive speech synthesis, but it often fails to imitate unseen speaking styles from uncommon scenarios (e.

By Tianxin Xie, Chenxing Li, Dong Yu, Li Liu

diffusion reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 2

Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models

arXiv:2602. 23197v2 Announce Type: replace-cross Abstract: Transformer-based large language models exhibit in-context learning, enabling adaptation to downstream tasks via few-shot prompting with demonstrations.

By Chungpa Lee, Jy-yong Sohn, Kangwook Lee

llms fine-tuning

arXiv AI

Jul 1

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

arXiv:2511. 16757v2 Announce Type: replace-cross Abstract: Audio-language pretraining (ALP) holds promise for learning general-purpose audio representation, yet remains underexplored.

By Wei-Cheng Tseng, Xuanru Zhou, Mingyue Huo, Yiwen Shao, Hao Zhang, Dong Yu

llms

Related stories

MetaSICL: Globalizing Auditory LLMs for Underserved Speakers and Languages via Meta Speech In-Context Learning

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

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

VoiceTTA: Enhancing Zero-Shot Text-to-Speech via Reinforcement Learning-Based Test-Time Adaptation

Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models

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