Stay Within Your Bounds: Distance-Guided Decoding for Guaranteed Context-Free Grammar Compliance
cs.AI, cs.CL, cs.FL, cs.LG
Submitted: 2026-08-28
Updated: 2026-08-28
Comments: EMNLP 2026 Findings, Long Paper
License: http://creativecommons.org/licenses/by-sa/4.0/
The gist: Grammar-constrained decoding helps large language models produce syntactically valid structured outputs, such as code, JSON, and SQL.
Abstract
Grammar-constrained decoding helps large language models produce syntactically valid structured outputs, such as code, JSON, and SQL. For context-free grammars, many practical decoders enforce local prefix feasibility: each token must keep the current prefix extendable to some valid completion. Yet, under tokenizer-grammar mismatch and finite token budgets, feasible prefixes may still fail to reach acceptance. We propose a lookahead-guided decoding framework for context-free grammars based on pushdown automata. Offline, we compute bounded pushdown summaries with reachability labels and upper-bound distances to acceptance. Online, these estimates guide horizon-aware pruning and beam search. The resulting decoder is syntactically sound: every output is accepted by the target grammar. Experiments on JSON, SQL, and Linear Temporal Logic (LTL) show both consistent syntactic validity and improved completion quality over existing baselines.
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