用大模型对话帮用户把模糊需求变成可解的优化问题
LAPPI: Interactive Optimization with LLM-Assisted Preference-Based Problem Instantiation
- 通过自然语言对话引导用户定义候选项与偏好
- 用户研究显示生成方案优于传统方法和提示工程
- 适用于旅行规划等需灵活建模的场景
许多现实任务，如行程规划或膳食安排，可建模为组合优化问题。但使用优化求解器对普通用户而言困难，因需完成问题实例化：定义候选项目、分配偏好分值并设定约束。本文提出LAPPI（LLM辅助的基于偏好的问题实例化），一种交互式方法，利用大语言模型（LLMs）协助用户完成该过程。通过自然语言对话，系统将模糊偏好转化为明确的优化问题，并交由现有求解器生成解。在行程规划的用户研究中，该方法有效捕捉用户偏好，生成可行方案且表现优于传统方法与提示工程策略。此外，我们进一步验证了LAPPI在另一应用场景中的泛化能力。
原文摘要 · Abstract (English)
Many real-world tasks, such as trip planning or meal planning, can be formulated as combinatorial optimization problems. However, using optimization solvers is difficult for end users because it requires problem instantiation: defining candidate items, assigning preference scores, and specifying constraints. We introduce LAPPI (LLM-Assisted Preference-based Problem Instantiation), an interactive approach that uses large language models (LLMs) to support users in this instantiation process. Through natural language conversations, the system helps users transform vague preferences into well-defined optimization problems. These instantiated problems are then passed to existing optimization solvers to generate solutions. In a user study on trip planning, our method successfully captured user preferences and generated feasible plans that outperformed both conventional and prompt-engineering approaches. We further demonstrate LAPPI's versatility by adapting it to an additional use case.
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