Large language models can produce creative and diverse responses. However, to integrate them into current developer workflows, it is essential to constrain their outputs to follow specific formats or standards. In this work, we surveyed 51 experienced industry professionals to understand the range of scenarios and motivations driving the need for output constraints from a user-centered perspective. We identified 134 concrete use cases for constraints at two levels: low-level, which ensures the output adhere to a structured format and an appropriate length, and high-level, which requires the output to follow semantic and stylistic guidelines without hallucination. Critically, applying output constraints could not only streamline the currently repetitive process of developing, testing, and integrating LLM prompts for developers, but also enhance the user experience of LLM-powered features and applications. We conclude with a discussion on user preferences and needs towards articulating intended constraints for LLMs, alongside an initial design for a constraint prototyping tool.

中文翻译：

---

“我们需要结构化输出”：面向以用户为中心的大型语言模型输出约束

大型语言模型可以产生创造性和多样化的响应。然而，要将它们集成到当前的开发人员工作流程中，必须限制它们的输出遵循特定的格式或标准。在这项工作中，我们调查了 51 位经验丰富的行业专业人士，从以用户为中心的角度了解推动输出限制需求的场景和动机的范围。我们确定了两个级别约束的 134 个具体用例：低级，确保输出遵循结构化格式和适当的长度；高级，要求输出遵循语义和风格指南，而不产生幻觉。至关重要的是，应用输出约束不仅可以简化当前为开发人员开发、测试和集成 LLM 提示的重复过程，还可以增强 LLM 支持的功能和应用程序的用户体验。最后，我们讨论了用户偏好和需求，以阐明法学硕士的预期约束，以及约束原型工具的初始设计。