arXiv：cs.LG· Everett Rush, David J. Icove, Ari Kim, Byung H. Park, Michael A. Langston·· 3 小时前AI 评分30
用大语言模型克服解释结构建模的挑战
Overcoming Challenges of Interpretive Structural Modeling with Large Language Models
AI 导读
研究探索将大语言模型作为"不完美专家"引入解释结构建模（ISM），以替代依赖专家反复交互的传统流程。对比成对、k-wise、逐行和全图四种因果图发现方法后，逐行法（SHD=160，F1-score=0.77）与全图法（SHD=135，F1-score=0.73）表现最佳。
正文
Abstract:Interpretive Structural Modeling (ISM) is a well-known process for multi-criteria decision making. The success of ISM over other methodologies is its ability to model causal relationships, the binary scale of factors, and resulting hierarchical representation. Traditionally, the modeling process is performed by repeated interactions with subject matter experts until consensus is reached. This process is tedious, labor-intense, and most importantly limits the ability of ISM to scale to studies with hundreds of variables. Drawing on existing work of causal graph discovery with large language models (LLM) as imperfect experts, this work explores an integrated LLM-ISM approach for ISM. Pairwise, k-wise, rowwise, and full graph discovery methodologies are compared and evaluated. It is shown that causal graph discovery methods for ISM perform best using rowwise (SHD=160, F1-score=0.77) and full graph methods (SHD=135, F1-score=0.73).
| Comments: | This preprint has not undergone peer review or any post-submission improvements or corrections |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.02254 [cs.LG] |
| (or arXiv:2610.02254v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02254 arXiv-issued DOI via DataCite |
Submission history
From: Everett Rush [view email]
[v1]
Wed, 30 Sep 2026 19:25:18 UTC (78 KB)
来源：arXiv：cs.LG · arxiv.org