arXiv AI August 10, 2026 By Haolong Zheng, Siyin Wang, Zengrui Jin, Mark Hasegawa-Johnson

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.

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arXiv AI

Sep 1

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

arXiv:2606.02615v2 Announce Type: replace-cross Abstract: Few-shot prompting provides an effective way to adapt auditory large language models to low-resource tasks such as children's speech recognit...

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

llms reinforcement-learning multimodal

arXiv AI

Jun 3

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.

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

llms reinforcement-learning multimodal

arXiv AI

Sep 23

Listen Then Reason: Perception-Grounded Test-Time Reinforcement Learning for Large Audio-Language Models

arXiv:2609.23589v1 Announce Type: cross Abstract: Large audio-language models (LALMs) are increasingly used for a broader range of audio reasoning tasks. These models typically incorporate audio repr...

By Jiaheng Dong, Xiaofeng Yu, Jean Honorio, Abhirup Ghosh, Hong Jia, Ting Dang

llms reinforcement-learning multimodal benchmarks

arXiv Computation and Language

Sep 17

Encoder Awakening via Adapters: Effective Domain-Adaptive Fine-tuning of Speech-LLMs

arXiv:2609.17981v1 Announce Type: cross Abstract: Speech Large Language Models (Speech-LLMs), typically built from a pre-trained speech encoder, a modality projector, and an LLM fine-tuned with Low-R...

By Mohan Shi, Zilai Wang, Natarajan Balaji Shankar, Kaiyuan Zhang, Eray Eren, Abeer Alwan

llms fine-tuning multimodal benchmarks

Hugging Face Trending Papers

Sep 16

Encoder Awakening via Adapters: Effective Domain-Adaptive Fine-tuning of Speech-LLMs

Speech Large Language Models (Speech-LLMs), typically built from a pre-trained speech encoder, a modality projector, and an LLM fine-tuned with Low-Rank Adapters (LoRA), have shown strong Automatic Sp...

llms fine-tuning multimodal benchmarks

arXiv AI

Jul 14

Encoder-Side Neuron Identification and Amplification for Acoustic Perception in Large Audio-Language Models

arXiv:2607. 11801v1 Announce Type: cross Abstract: Large audio-language models (LALMs) often underperform on fine-grained, non-semantic attributes of speech, such as a speaker's emotion, despite strong performance on speech content.

By Yu-Han Huang, Chih-Kai Yang, Ke-Han Lu, An-Yu Cheng, Hung-yi Lee

llms fine-tuning

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

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

Listen Then Reason: Perception-Grounded Test-Time Reinforcement Learning for Large Audio-Language Models

Encoder Awakening via Adapters: Effective Domain-Adaptive Fine-tuning of Speech-LLMs

Encoder Awakening via Adapters: Effective Domain-Adaptive Fine-tuning of Speech-LLMs

Encoder-Side Neuron Identification and Amplification for Acoustic Perception in Large Audio-Language Models