Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation
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Abstract (arXiv)
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× speedup over unconstrained decoding -- thereby outperforming existing approaches by a wide margin.
Key facts. Cited by 101 papers in the map (data through 14 Oct 2026). Builds on 17 of its 25 references that are in the map. First posted on arXiv 7 Feb 2024.
Builds on
17 of 25 references.
References · 17
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- 2023Grammar-Constrained Decoding for Structured NLP Tasks without Finetuningcited by 197
- 2022Synchromesh: Reliable code generation from pre-trained language modelscited by 156
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