@inproceedings{zhao-etal-2026-symbolicthought,
    title = "{S}ymbolic{T}hought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding",
    author = "Zhao, Runcong  and
      Zhu, Qinglin  and
      Xu, Hainiu  and
      Liang, Bin  and
      Gui, Lin  and
      He, Yulan",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-demo.4/",
    pages = "36--46",
    ISBN = "979-8-89176-392-0",
    abstract = "Understanding character relationships is essential for interpreting complex narratives and conducting socially grounded AI research. However, manual annotation is time-consuming and low in coverage, while large language models (LLMs) often produce hallucinated or logically inconsistent outputs. We present SymbolicThought, a human-in-the-loop framework that combines LLM-based extraction with symbolic reasoning. The system constructs editable character relationship graphs, refines them using seven types of logical constraints, and enables real-time validation and conflict resolution through an interactive interface. To support logical supervision and explainable social analysis, we release a dataset of 160 interpersonal relationships with corresponding logical structures. Experiments show that SymbolicThought improves annotation accuracy and consistency while significantly reducing time cost, offering a practical tool for narrative understanding, explainable AI, and LLM evaluation. The source code and dataset are publicly available on GitHub (https://github.com/BLPXSPG/SymbolicThought)."
}