Large language model (LLM) applications, such as ChatGPT, are a powerful tool for online information-seeking (IS) and problem-solving tasks. However, users still face challenges initializing and refining prompts, and their cognitive barriers and biased perceptions further impede task completion. These issues reflect broader challenges identified within the fields of IS and interactive information retrieval (IIR). To address these, our approach integrates task context and user perceptions into human-ChatGPT interactions through prompt engineering. We developed a ChatGPT-like platform integrated with supportive functions, including perception articulation, prompt suggestion, and conversation explanation. Our findings of a user study demonstrate that the supportive functions help users manage expectations, reduce cognitive loads, better refine prompts, and increase user engagement. This research enhances our comprehension of designing proactive and user-centric systems with LLMs. It offers insights into evaluating human-LLM interactions and emphasizes potential challenges for under served users.

翻译：大语言模型（LLM）应用，如ChatGPT，已成为在线信息检索（IS）和问题求解任务中的强大工具。然而，用户在初始化和优化提示词时仍面临挑战，其认知障碍和偏差感知进一步阻碍了任务完成。这些问题反映了信息检索与交互式信息检索（IIR）领域已识别的更广泛挑战。为此，我们通过提示工程将任务上下文和用户感知融入人-ChatGPT交互中，开发了一个集成支持功能的类ChatGPT平台，这些功能包括感知表达、提示建议和对话解释。我们的用户研究表明，支持功能能帮助用户管理预期、降低认知负荷、更好地优化提示词并提升用户参与度。本研究深化了对设计基于LLM的用户主动型与中心化系统的理解，为评估人-LLM交互提供了见解，并强调了服务不足用户可能面临的潜在挑战。