让外行用日常语言问模拟问题，LLM助力普通人轻松用仿真系统。
Broadening Access to Simulations for End-Users via Large Language Models: Challenges and Opportunities
- 用自然语言匹配最相关仿真模型，实现跨领域查询。
- 自动重写问题并生成澄清提问，解决语义模糊难题。
- 适合非专业用户、研究者及伦理设计者参考。
大型语言模型（LLMs）正被广泛用于构建智能虚拟助手，辅助用户与系统交互，如在营销领域。尽管已有讨论涉及建模与仿真（M&S），但该领域主要关注代码生成或结果解释。本文探讨利用LLMs拓宽仿真系统访问权限的可能性，使非仿真领域的终端用户能以日常语言提出“如果……会怎样”的假设性问题。我们分析了构建端到端系统的三大阶段：首先，在多个可用仿真模型中，将文本查询映射至最相关的模型；其次，若无法直接匹配，则自动重写查询并生成澄清问题；最后，生成仿真结果并为其提供决策上下文。该愿景揭示了横跨建模与仿真、大语言模型、信息检索及伦理学的长期研究机遇。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are becoming ubiquitous to create intelligent virtual assistants that assist users in interacting with a system, as exemplified in marketing. Although LLMs have been discussed in Modeling & Simulation (M&S), the community has focused on generating code or explaining results. We examine the possibility of using LLMs to broaden access to simulations, by enabling non-simulation end-users to ask what-if questions in everyday language. Specifically, we discuss the opportunities and challenges in designing such an end-to-end system, divided into three broad phases. First, assuming the general case in which several simulation models are available, textual queries are mapped to the most relevant model. Second, if a mapping cannot be found, the query can be automatically reformulated and clarifying questions can be generated. Finally, simulation results are produced and contextualized for decision-making. Our vision for such system articulates long-term research opportunities spanning M&S, LLMs, information retrieval, and ethics.
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