让不懂优化的用户用自然语言和优化模型对话。
OptiChat: Bridging Optimization Models and Practitioners with Large Language Models
- 用大模型+代码生成，让非专家能自然语言操作优化模型。
- 在真实任务中实现即时准确响应，错误率低于15%。
- 适合业务人员、初学者快速理解与调试优化模型。
优化模型广泛应用于各类决策问题，但通常由专家开发，由缺乏优化背景的从业者使用。这导致使用者难以独立理解和利用模型。为此，我们提出OptiChat，一个基于自然语言的对话系统，帮助从业者解释模型结构、诊断不可行性、分析敏感性、检索信息、评估修改方案并获得反事实解释。通过为大语言模型（LLMs）注入针对优化模型的功能调用与代码生成能力，OptiChat实现无缝交互，并显著降低幻觉风险。我们构建了一个新数据集用于评估其在解释优化模型方面的能力。实验表明，OptiChat能有效弥合优化模型与使用者之间的鸿沟，提供自主、准确且即时的响应。
原文摘要 · Abstract (English)
Optimization models have been applied to solve a wide variety of decision-making problems. These models are usually developed by optimization experts but are used by practitioners without optimization expertise in various application domains. As a result, practitioners often struggle to interact with and draw useful conclusions from optimization models independently. To fill this gap, we introduce OptiChat, a natural language dialogue system designed to help practitioners interpret model formulation, diagnose infeasibility, analyze sensitivity, retrieve information, evaluate modifications, and provide counterfactual explanations. By augmenting large language models (LLMs) with functional calls and code generation tailored for optimization models, we enable seamless interaction and minimize the risk of hallucinations in OptiChat. We develop a new dataset to evaluate OptiChat's performance in explaining optimization models. Experiments demonstrate that OptiChat effectively bridges the gap between optimization models and practitioners, delivering autonomous, accurate, and instant responses.
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