Suffix-Constrained Greedy Search Algorithms for Causal Language Models

Abstract:Large language models (LLMs) are powerful tools that have found applications beyond human-machine interfaces and chatbots. Beside free-form generation, there has been an interest in constrained generation, a setting where LLMs are constrained to generate well-formed outputs with respect to the language defined by a formal grammar. Although appealing, this setting may be over restrictive for downstream applications. For example, many LLM tasks require the model to reason freely before generating its final response in a specific format. In this work, we introduce suffix-constrained generation, a constrained generation setting in which only the end of the response is constrained by a grammar, a scenario that is not supported by existing constrained generation methods. We introduce several suffix-constrained generation algorithms that are based on greedy search. We experiment on several datasets, and show that our approach allows to guarantee suffix constraints without having a negative impact on results, and even improving them in many settings.

Computation and Language (cs.CL)

arXiv:2603.01243 [cs.CL]

(or arXiv:2603.01243v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2603.01243 Focus to learn more arXiv-issued DOI via DataCite

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