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    <abstract>Few-shot prompting provides an effective way to adapt auditory large language models to low-resource tasks such as children’s speech recognition. However, most auditory large language models are not explicitly trained to perform inference in this demonstration-conditioned format, limiting the extent to which they can benefit from In-Context Learning (ICL). To address this limitation, we introduce Few-Shot Aware GRPO (FSA-GRPO), an RL-based post-training recipe that uses a specially designed reward to encourage the model to leverage few-shot demonstrations, thereby strengthening its few-shot adaptation ability. Notably, training with only 2k high-resource adult ASR utterances improves the model’s general few-shot adaptation ability, yielding gains not only in children’s speech recognition (53.9% relative WER reduction without any in-domain training) but also in multilingual ASR (including low-resource languages), speech translation, and audio understanding. We further study data selection and the weight and similarity cutoffs of the auxiliary reward to identify an effective training recipe. Our experiments show that when in-domain data are unavailable or cannot be used for training, FSA-GRPO is more effective than direct tuning on related out-of-domain data.</abstract>
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%0 Conference Proceedings
%T FSA-GRPO: Teaching Auditory LLMs to Use Few-Shot Demonstrations
%A Zheng, Haolong
%A Wang, Siyin
%A Fan, Xulin
%A Jin, Zengrui
%A Hasegawa-Johnson, Mark A.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zheng-etal-2026-fsa
%X Few-shot prompting provides an effective way to adapt auditory large language models to low-resource tasks such as children’s speech recognition. However, most auditory large language models are not explicitly trained to perform inference in this demonstration-conditioned format, limiting the extent to which they can benefit from In-Context Learning (ICL). To address this limitation, we introduce Few-Shot Aware GRPO (FSA-GRPO), an RL-based post-training recipe that uses a specially designed reward to encourage the model to leverage few-shot demonstrations, thereby strengthening its few-shot adaptation ability. Notably, training with only 2k high-resource adult ASR utterances improves the model’s general few-shot adaptation ability, yielding gains not only in children’s speech recognition (53.9% relative WER reduction without any in-domain training) but also in multilingual ASR (including low-resource languages), speech translation, and audio understanding. We further study data selection and the weight and similarity cutoffs of the auxiliary reward to identify an effective training recipe. Our experiments show that when in-domain data are unavailable or cannot be used for training, FSA-GRPO is more effective than direct tuning on related out-of-domain data.
%U https://aclanthology.org/2026.findings-emnlp.388/
%P 8192-8207

Markdown (Informal)

[FSA-GRPO: Teaching Auditory LLMs to Use Few-Shot Demonstrations](https://aclanthology.org/2026.findings-emnlp.388/) (Zheng et al., Findings 2026)

• FSA-GRPO: Teaching Auditory LLMs to Use Few-Shot Demonstrations (Zheng et al., Findings 2026)

ACL

• Haolong Zheng, Siyin Wang, Xulin Fan, Zengrui Jin, and Mark A. Hasegawa-Johnson. 2026. FSA-GRPO: Teaching Auditory LLMs to Use Few-Shot Demonstrations. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 8192–8207, Budapest, Hungary. Association for Computational Linguistics.