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About this paper
Yung-Shen Hsia, Fang Yu, Jie-Hong Roland Jiang
A Neuro-Symbolic Compliance Framework integrates Large Language Models (LLMs) with Satisfiability Modulo Theories (SMT) solvers to automate financial legal analysis. The framework translates legal texts and case facts into SMT constraints, enabling precise violation identification and optimization-based suggestions for minimal factual changes to restore compliance, demonstrating significant efficiency and accuracy improvements over LLM-only approaches on real-world financial penalty cases.
Key insights
- Combining LLMs with SMT solvers bridges the gap between natural legal language interpretation and machine-verifiable logical reasoning, enhancing both interpretability and formal verifiability.
- SMT-based optimization can precisely identify the minimal factual changes required to restore legal compliance, providing actionable guidance for remediation.
- A modular multi-agent architecture enables robust, adaptable, and explainable legal reasoning, facilitating automated correction and human-in-the-loop adaptability.
Summary by alphaXiv.
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