Neurosymbolic Language Reasoning as Satisfiability Modulo Theory
This work addresses the challenge of interleaving textual and logical reasoning in natural language understanding for domains with partial logical structure, such as content moderation and legal documents, representing an incremental advance by extending neurosymbolic methods beyond fully formalizable tasks.
The paper tackles the problem of unreliable reasoning in large language models by introducing Logitext, a neurosymbolic language that represents documents as natural language text constraints, enabling joint textual-logical reasoning through SMT solving, and experiments show improvements in accuracy and coverage on benchmarks like LegalBench and Super-Natural Instructions.
Natural language understanding requires interleaving textual and logical reasoning, yet large language models often fail to perform such reasoning reliably. Existing neurosymbolic systems combine LLMs with solvers but remain limited to fully formalizable tasks such as math or program synthesis, leaving natural documents with only partial logical structure unaddressed. We introduce Logitext, a neurosymbolic language that represents documents as natural language text constraints (NLTCs), making partial logical structure explicit. We develop an algorithm that integrates LLM-based constraint evaluation with satisfiability modulo theory (SMT) solving, enabling joint textual-logical reasoning. Experiments on a new content moderation benchmark, together with LegalBench and Super-Natural Instructions, show that Logitext improves both accuracy and coverage. This work is the first that treats LLM-based reasoning as an SMT theory, extending neurosymbolic methods beyond fully formalizable domains.