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. 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 few-shot prompting. 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 demonstrat
The proliferation of auditory large language models (LLMs) requires more efficient adaptation methods, making few-shot learning critical for immediate deployment and task specialization.
Improving few-shot learning for auditory LLMs expands their applicability to low-resource tasks, accelerating development and deployment in specialized domains like children's speech.
Auditory LLMs will become more adaptable and effective in niche applications without extensive retraining, leading to faster iteration and broader functional scope.
- · AI developers
- · Speech recognition technology
- · Education technology
- · Voice AI startups
- · Tasks requiring large labeled auditory datasets
- · Traditional fine-tuning methods
- · One-size-fits-all auditory AI solutions
Auditory LLMs will gain increased performance and versatility in specialized, low-resource sound domains.
This advancement could democratize access to sophisticated speech recognition and auditory processing for smaller organizations and less common languages/dialects.
The reduced data dependency might lead to a proliferation of highly specialized auditory AI agents integrated into everyday objects and services.
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Read at arXiv cs.AI