Imagine a world where your voice assistant understands you perfectly, even when you ask something it's never heard before. This is the promise of zero-shot spoken language understanding (SLU), and researchers are making exciting strides. A new study explores using Whisper, a powerful speech processing model, to achieve zero-shot SLU without relying on massive, complex language models. Traditionally, training SLU models requires huge amounts of labeled data, making it costly and time-consuming to adapt to new domains. This research proposes a clever solution: reframing SLU tasks like intent classification and slot filling as question-answering problems. By prompting Whisper with cleverly designed questions and utilizing a technique called prefix-tuning, the model can deduce the meaning of spoken utterances without prior training on specific semantic labels. The results are impressive. The Whisper-based system achieves a remarkable improvement in accuracy compared to existing benchmarks, demonstrating its potential for real-world applications. It even performs on par with larger, more complex modular systems while using significantly fewer parameters, making it a more efficient and practical solution. One of the key innovations is how semantic questions are generated. Instead of relying on hand-crafted templates, the researchers use large language models (LLMs) to create questions based on descriptions and example utterances, leading to more robust and accurate understanding. While this research demonstrates a significant leap forward, challenges remain. Future work involves exploring more sophisticated question generation methods and refining the prefix-tuning process to further enhance performance. This research opens exciting new avenues for developing more adaptable and efficient voice assistants, making the dream of truly conversational AI a tangible reality.