SHIM：用解析器状态做轻量偏置校正的语法约束解码

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Abstract:Grammar Constrained Decoding (GCD) forces Language Models (LMs) to produce syntactically valid outputs by masking out non-conforming tokens at each step. However, because masking only checks whether each token is valid so far, the resulting distribution over complete outputs diverges from the LM's own distribution conditioned on the grammar, biasing generation toward valid but suboptimal outputs. Online sampling can restore this distribution, but only through costly iterative resampling. Our key insight is that the parser and lexer states that GCD tools already maintain carry a strong signal about future grammatical validity. We introduce SHIM, a lightweight, offline-trained correction of the LM's next-token probabilities, conditioned on this syntactic and lexical state together with candidate next tokens. Since GCD tools already compute these states, SHIM leaves the LM itself untouched. Across bit-vector and text-to-SQL grammars, this correction substantially narrows the gap to the LM's grammar-conditioned distribution compared to masking and online sampling, while running at nearly masking's speed. Even a variant that sees only the next token can improve on both baselines, making SHIM usable with GCD tools that do not expose their parser state.

Comments:	10 pages, 5 figures
Subjects:	Computation and Language (cs.CL); Machine Learning (cs.LG)
ACM classes:	I.2.7; F.4.2
Cite as:	arXiv:2608.10137 [cs.CL]
	(or arXiv:2608.10137v2 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2608.10137 arXiv-issued DOI via DataCite

Submission history

From: Işıl Özgü [view email]
[v1] Mon, 10 Aug 2026 18:52:43 UTC (233 KB)
[v2] Wed, 7 Oct 2026 00:39:27 UTC (235 KB)