Human-Centered Intention Refinement for LLM-based Domain-Data Analysis Using Iterative Prompt Generation

An intention refinement method for domain-data analysis using large language models is proposed. It employs a “domain-framing prompt” to relate a domain expert's interests with relevant domain data. The initial responses are followed by “probing prompts” that suggest possible follow-up questions to provide further insight. The suggestions aim to support the domain expert in recognizing alternative analysis steps that may be difficult to notice depending on their analysis skills. The main contribution of this study is the proposal of this human-centered process to support domain experts in interacting with an LLM effectively. An experimental implementation demonstrates the method's effectiveness in expanding user interests and generating exploratory questions to assist intention refinement.