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This paper proposes a neuro-symbolic decoding method that filters LLM outputs against a formal specification at each generation step, going beyond the syntax-only checks of existing constrained decoding. The forward-only variant guarantees compliance by construction and cuts repeated inference relative to rejection sampling, while the backtracking variant addresses cases where the model is forced into choices it would itself reject. Results are reported on configuration and constraint-solving benchmarks, so practitioners should check how well the approach transfers to other specification types and whether the symbolic overhead holds at scale.
Specification-Guided Generation for Large Language Models
arXiv:2610.41511v1 Announce Type: new Abstract: Large language models (LLMs) can generate structured outputs, but existing constrained-decoding methods mainly enforce syntax and cannot directly enforce satisfaction of a formal specification. We introduce a neuro-symbolic, specification-guided decoding framework that combines LLM generation with symbolic reasoning. At each step, the framework refines the decoding grammar by filtering out values that cannot be extended to an output satisfying the specification. We present a forward-only variant that guarantees specification satisfaction by construction, and a backtracking variant that revises earlier feasible choices when their downstream consequences leave only values that the LLM itself judges unsuitable. The framework is model-agnostic, requires no retraining, and builds on existing grammar-constrained decoding mechanisms. Experiments on configuration and constraint-solving benchmarks show that the forward-only variant reliably produces specification-compliant outputs while reducing repeated LLM inference compared with rejection sampling and incurring modest symbolic-reasoning overhead. The backtracking variant further reduces cases in which the model is forced to select values that it has itself judged unsuitable, thereby substantially improving adherence to the LLM's stated interpretation of the prompt.
Abstract:Large language models (LLMs) can generate structured outputs, but existing constrained-decoding methods mainly enforce syntax and cannot directly enforce satisfaction of a formal specification. We introduce a neuro-symbolic, specification-guided decoding framework that combines LLM generation with symbolic reasoning. At each step, the framework refines the decoding grammar by filtering out values that cannot be extended to an output satisfying the specification. We present a forward-only variant that guarantees specification satisfaction by construction, and a backtracking variant that revises earlier feasible choices when their downstream consequences leave only values that the LLM itself judges unsuitable. The framework is model-agnostic, requires no retraining, and builds on existing grammar-constrained decoding mechanisms. Experiments on configuration and constraint-solving benchmarks show that the forward-only variant reliably produces specification-compliant outputs while reducing repeated LLM inference compared with rejection sampling and incurring modest symbolic-reasoning overhead. The backtracking variant further reduces cases in which the model is forced to select values that it has itself judged unsuitable, thereby substantially improving adherence to the LLM's stated interpretation of the prompt.
Artificial Intelligence (cs.AI)
arXiv:2610.41511 [cs.AI]
(or arXiv:2610.41511v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.41511 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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