LCSL: A Symbolic Rule Language and Reasoning Framework for Enhancing Legal Compliance in Large Language Models

Addressing the difficulties of Large Language Models (LLMs) in handling non-monotonic logic and opaque reasoning in legal compliance, we propose the LCSL-Framework, based on a Legal Compliance Symbolic Language. The framework combines Prolog-style predicate conditions with defeasible reasoning to decouple legal rules into prohibitive rules and defeaters. Its central design connects contextual condition interpretation to targeted conclusion revision: ATTACKS links an exception to its prohibition, and OVERRIDES withdraws that inference while other prohibitions remain subject to judgment. An LLM constructs reusable rule blocks and interprets a three-phase protocol for prohibition assessment, exception verification, and conflict adjudication. Experiments on Chinese Advertising Law, Copyright Law, and Hearsay tasks show the highest $F_1$ among the evaluated baselines, including SymbCoT. On Hearsay and Abs-Real, LCSL exceeds SePO by 5.8 and 7.6 percentage points. Abs-Real ablations reduce $F_1$ by 7.1 points without the rule representation and 4.2 without staged reasoning, supporting both aspects of the design. Case analysis illustrates the connection between conditions, exceptions, and judgments; OPP-115-derived classification provides auxiliary privacy evaluation.