LAPPI：基于LLM的偏好驱动问题实例化交互优化
LAPPI: Interactive Optimization with LLM-Assisted Preference-Based Problem Instantiation
AI总结：
LAPPI通过LLM辅助用户将模糊偏好转化为明确的优化问题，提升终端用户在旅行计划等任务中的问题实例化效率和解决方案质量。
摘要：
许多现实任务，如旅行计划或饮食计划，可以被建模为组合优化问题。然而，对于终端用户来说，使用优化求解器是困难的，因为这需要问题实例化：定义候选项目、分配偏好分数并指定约束。我们引入了LAPPI（LLM辅助偏好驱动问题实例化），一种交互式方法，利用大型语言模型（LLMs）来帮助用户完成此实例化过程。通过自然语言对话，系统帮助用户将模糊的偏好转化为明确的优化问题。这些实例化的问题随后传递给现有的优化求解器以生成解决方案。在旅行计划的用户研究中，我们的方法成功捕捉了用户偏好，并生成了优于传统方法和提示工程方法的可行计划。我们进一步通过将其适应到另一个用例来展示LAPPI的通用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
