让聊天机器人像界面一样明确提交和重置，减少用户困惑
Bridging UI Design and chatbot Interactions: Applying Form-Based Principles to Conversational Agents
- 在提示中引入提交与重置的显式任务设计
- 多轮对话一致性提升，用户满意度显著改善
- 适合需要复杂交互的领域专用聊天机器人
特定领域的聊天机器人应用常涉及多步骤交互，如筛选条件调整、多项选择或对比操作。传统图形界面通过明确的“提交”（提交数据）和“重置”（丢弃数据）操作，使后端系统能清晰追踪用户意图。而聊天机器人依赖细微语言线索，易造成混淆和上下文管理不全。本文提出将这些界面设计范式——确认（类似提交）和上下文切换（类似重置）——作为大语言模型提示中的显式任务。通过结构化记录用户确认、重置动作及思维链（CoT）推理，实现对话清晰性提升，降低用户困惑，并使领域聊天机器人的交互逻辑与后端系统对齐。我们在酒店预订和客户管理场景中验证该方法，显著提升了多轮任务连贯性、用户满意度和执行效率。
原文摘要 · Abstract (English)
Domain specific chatbot applications often involve multi step interactions, such as refining search filters, selecting multiple items, or performing comparisons. Traditional graphical user interfaces (GUIs) handle these workflows by providing explicit "Submit" (commit data) and "Reset" (discard data) actions, allowing back-end systems to track user intent unambiguously. In contrast, conversational agents rely on subtle language cues, which can lead to confusion and incomplete context management. This paper proposes modeling these GUI inspired metaphors acknowledgment (submit like) and context switching (reset-like) as explicit tasks within large language model (LLM) prompts. By capturing user acknowledgment, reset actions, and chain of thought (CoT) reasoning as structured session data, we preserve clarity, reduce user confusion, and align domain-specific chatbot interactions with back-end logic. We demonstrate our approach in hotel booking and customer management scenarios, highlighting improvements in multi-turn task coherence, user satisfaction, and efficiency.
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