用信息检索选例，提升少样本语音理解的提示效果。
Leveraging Information Retrieval to Enhance Spoken Language Understanding Prompts in Few-Shot Learning
- 通过信息检索筛选相关示例，构建增强型提示
- 词汇级检索方法显著提升性能，且不增加提示长度
- 适合资源有限的语音理解场景，尤其小语种或新任务
理解用户查询是家庭助手、预订系统和推荐系统等应用的核心。因此，开发准确的语音语言理解（SLU）方法对保障系统可靠性至关重要。当前最先进的SLU技术依赖大量训练数据，但特定任务或语言的标注样本通常有限。与此同时，指令微调的大模型在少样本设置下，若提供充分提示，可在未见任务上表现出色。本文提出利用信息检索（IR）方法进行示例选择，构建用于SLU任务的增强提示。我们在多个SLU基准上评估该方法的有效性。实验结果表明，词汇级检索方法能显著提升性能，且无需增加提示长度。
原文摘要 · Abstract (English)
Understanding user queries is fundamental in many applications, such as home assistants, booking systems, or recommendations. Accordingly, it is crucial to develop accurate Spoken Language Understanding (SLU) approaches to ensure the reliability of the considered system. Current State-of-the-Art SLU techniques rely on large amounts of training data; however, only limited annotated examples are available for specific tasks or languages. In the meantime, instruction-tuned large language models (LLMs) have shown exceptional performance on unseen tasks in a few-shot setting when provided with adequate prompts. In this work, we propose to explore example selection by leveraging Information retrieval (IR) approaches to build an enhanced prompt that is applied to an SLU task. We evaluate the effectiveness of the proposed method on several SLU benchmarks. Experimental results show that lexical IR methods significantly enhance performance without increasing prompt length.
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