arXiv cs.CL· Gokhan Ceyhan·· 12 小时前AI 评分27
ParetoChat：基于 LLM 的交互式多目标优化界面
ParetoChat: An LLM-Based Interface for Interactive Multi-Objective Optimization
AI 导读
ParetoChat 是一个基于 LLM 的专家智能体界面，可将用户自然语言输入转换为结构化标量化参数，调用优化系统并迭代收集反馈。其多智能体架构分离输入处理、MCDM 专业知识、方案展示与反馈处理，在多目标二进制背包和集合划分实例上对比 DESDEO 中的参考点与 NIMBUS 方法，能高效引导搜索至符合决策者偏好的解。
正文 · 原文
Abstract:Multi-objective optimization models are often deployed in settings where decision makers (DMs) must balance some conflicting criteria. In practice, however, stakeholders must translate their qualitative preferences between different criteria into numerical weights, priorities, or reference points required by optimization methods, which can be difficult to specify and interpret. This paper proposes \emph{ParetoChat}, an LLM (large language model)-based expert-agent interface for interactive multi-objective optimization. The agent translates natural-language user input into structured scalarization parameters, invokes an optimization system, presents candidate solutions, and iteratively elicits feedback. We describe a multi-agent architecture that separates input processing, multi-criteria decision-making (MCDM) expertise, solution presentation, and feedback processing. We then present a simulation study using an LLM-based DM agent and benchmark ParetoChat against interactive methods implemented in DESDEO, including reference-point and NIMBUS methods. The computational experiments on multi-objective binary knapsack and set-partitioning instances show that ParetoChat can guide the search efficiently toward solutions that match the DM's preferences. An application to historical courier-order assignment instances illustrates how users can explore objective extremes and intermediate trade-offs through natural-language requests. The results indicate that LLM agents may provide a practical interaction layer between business stakeholders and optimization tools.
| Subjects: | Computation and Language (cs.CL); Optimization and Control (math.OC) |
| Cite as: | arXiv:2610.23062 [cs.CL] |
| (or arXiv:2610.23062v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.23062 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Gokhan Ceyhan [view email]
[v1]
Fri, 16 Oct 2026 07:39:39 UTC (1,337 KB)
来源：arXiv cs.CL · arxiv.org