Broadening Access to Simulations for End-Users via Large Language Models: Challenges and Opportunities
<|reference_start|>Broadening Access to Simulations for End-Users via Large Language Models: Challenges and Opportunities: 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.<|reference_end|>
@article{giabbanelli2024broadening,
    title={Broadening Access to Simulations for End-Users via Large Language
  Models: Challenges and Opportunities},
    author={Philippe J. Giabbanelli, Jose J. Padilla, Ameeta Agrawal},
    journal={arXiv preprint arXiv:2409.15290},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15290},
    primaryClass={cs.HC cs.AI}
}