Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation
Luca Beurer-Kellner Marc Fischer Martin Vechev
To ensure that text generated by large language models (LLMs) is in an
expected format, constrained decoding proposes to enforce strict formal
language constraints during generation. However, as we show in this work, not
only do such methods incur performance overhead during generation, but many of
them also significantly impair task accuracy, if they do not correctly align
the underlying LLM sub-word vocabularies with external constraints. To address
this, we present a novel decoding algorithm, DOMINO, that can enforce
constraints in a fully subword-aligned fashion, while leveraging
pre-computation and speculative decoding to achieve virtually no overhead and
in some cases even almost 2$\times$ speedup over unconstrained decoding --
thereby outperforming existing approaches by a wide margin.