@inproceedings{kong-etal-2026-modeling,
    title = "Modeling and Solving Stable Matching under Probabilistic Preferences with Large Language Models",
    author = "Kong, Yuqi  and
      Liu, Shiyu  and
      Li, Jiaxu  and
      Liu, Hongtao  and
      Qi, Qi  and
      Shen, Weiran",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1845/",
    pages = "37021--37033",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs) have recently demonstrated strong capability in understanding and simulating humans' decisions, suggesting a new way to use LLMs as tools to study social systems. We study two-sided-matching markets, such as dating and job matching. Classical matching models assume deterministic, strict preferences, which violate real-world setting. We focus on stable matching under stochastic decision behavior and use LLMs to simulate human-like preferences and probabilistic choice patterns. Based on this, we introduce Expected Blocking Pairs (EBP), a continuous measure to quantify stability that generalizes the classic blocking pair notion. We further propose a Hybrid GS{--}LLM matching method that integrates the celebrated Gale{--}Shapley (GS) algorithm with probabilistic acceptance decisions. Experiments show that the proposed hybrid method outperforms classical baselines in terms of stability, suggesting that LLMs provide a principled tool for modeling human decisions and for improving robustness of matching under uncertainty."
}