FSA-GRPO：训练听觉大语言模型使用少样本示例
FSA-GRPO: Teaching Auditory LLMs to Use Few-Shot Demonstrations
AI总结：
提出基于强化学习的后训练方法FSA-GRPO，通过专门设计的奖励机制鼓励模型利用少样本示例，增强其少样本适应能力，在儿童语音识别、语音翻译和音频理解等任务上取得提升。
摘要：
少样本提示为将听觉大语言模型适应低资源任务（如儿童语音识别）提供了一种有效方式。然而，大多数听觉大语言模型并未被明确训练以在这种示例条件格式下进行推理，限制了它们从少样本提示中获益的程度。为解决这一局限，我们引入了少样本感知GRPO（FSA-GRPO），一种基于强化学习的后训练方法，使用专门设计的奖励来鼓励模型利用少样本示例，从而增强其少样本适应能力。值得注意的是，仅使用高资源成人ASR数据进行训练即可提升模型的通用少样本适应能力，不仅在儿童语音识别中带来收益，在语音翻译和音频理解中也是如此。我们进一步研究了数据选择和辅助奖励加权，以确定有效的训练方案。实验表明，当域内数据不可用或无法用于训练时，FSA-GRPO比直接对相关域外数据进行微调更有效。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
