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About this paper
Brandon T. Willard, Rémi Louf
Normal Computing researchers developed a method for efficient guided generation from Large Language Models, reformulating the problem with finite-state machines to achieve an average O(1) computational cost per generated token. This approach empirically demonstrates significant speedups (e.g., over 5x faster than previous methods) while ensuring strict adherence to regular expressions and context-free grammars.
Key insights
- Text generation against a grammar can be framed as state transitions in a finite-state automaton, allowing for dynamic tracking of parsing progress.
- Pre-computing valid token continuations for all possible automaton states enables constant-time (average O(1)) determination of valid tokens during generation.
- Leveraging automata theory provides a robust and theoretically sound foundation for enforcing complex linguistic and structural constraints efficiently.
Summary by alphaXiv.
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