Large language model (LLM) powered chatbots are primarily text-based today, and impose a large interactional cognitive load, especially for exploratory or sensemaking tasks such as planning a trip or learning about a new city. Because the interaction is textual, users have little scaffolding in the way of structure, informational "scent", or ability to specify high-level preferences or goals. We introduce ExploreLLM that allows users to structure thoughts, help explore different options, navigate through the choices and recommendations, and to more easily steer models to generate more personalized responses. We conduct a user study and show that users find it helpful to use ExploreLLM for exploratory or planning tasks, because it provides a useful schema-like structure to the task, and guides users in planning. The study also suggests that users can more easily personalize responses with high-level preferences with ExploreLLM. Together, ExploreLLM points to a future where users interact with LLMs beyond the form of chatbots, and instead designed to support complex user tasks with a tighter integration between natural language and graphical user interfaces.

翻译：当前基于大型语言模型（LLM）的聊天机器人主要以文本形式交互，在探索性或意义构建任务（如规划旅行或了解新城市）中施加了较大的交互认知负荷。由于交互过程依赖纯文本，用户缺乏结构支撑、信息"线索"以及对高级偏好或目标的指定能力。我们提出ExploreLLM，该工具允许用户结构化思维、探索不同选项、在推荐与决策中导航，并更轻松地引导模型生成个性化响应。通过用户研究，我们发现在探索性或规划类任务中，用户认为ExploreLLM非常有用，因为它能为任务提供类似模式化的结构框架，并指导用户进行规划。研究还表明，借助ExploreLLM，用户能更便捷地通过高级偏好实现个性化响应。综上，ExploreLLM预示着未来用户与LLM的交互将超越聊天机器人形式，转而通过自然语言与图形用户界面的紧密集成，专为支持复杂用户任务而设计。