SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding

• URL: http://arxiv.org/abs/2507.04189v2
• Date: Sun, 13 Jul 2025 22:06:13 GMT
• Title: SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding
• Authors: Runcong Zhao, Qinglin Zhu, Hainiu Xu, Bin Liang, Lin Gui, Yulan He,
• Abstract summary: SymbolicThought is a human-in-the-loop framework that combines large language models with symbolic reasoning.<n>It constructs editable character relationship graphs, refines them using seven types of logical constraints, and enables real-time validation and conflict resolution.<n> Experiments show that SymbolicThought improves annotation accuracy and consistency while significantly reducing time cost.
• Score: 21.30887121514266
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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