🤖 AI Summary
Domain-specific chatbots suffer from ambiguous user intent, contextual fragmentation, and interaction disorganization during multi-turn interactions—such as conditional filtering, multi-option selection, and comparative operations—due to the absence of GUI-like “submit/reset” mechanisms. To address this, this work introduces, for the first time, a form-based Submit/Reset paradigm into conversational systems, explicitly modeling user confirmation behaviors and context-switching actions. Methodologically, we integrate formalized state tracking, fine-grained user action recognition, and chain-of-thought (CoT) reasoning, augmented by prompt engineering to enhance large language models’ capacity for structured dialogue state representation. Experiments in hotel booking and customer management domains demonstrate significant improvements: +28.6% in multi-turn task coherence, +32.1% in user satisfaction, and a reduction of 2.4 turns on average, indicating enhanced operational efficiency.
📝 Abstract
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.