# Efficient Guided Generation for Large Language Models

**Authors:** Brandon T. Willard, Rémi Louf  
**arXiv:** [2307.09702](https://arxiv.org/abs/2307.09702)  
**Date:** 2023-07-19  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.09702v3/

## Key points

- Authors propose a novel approach to neural text generation using finite-state machines
- Reformulating the problem allows for efficient text generation guided by regular expressions and context-free grammars
- Construction of an index over a language model's vocabulary enables model agnosticism and enforcement of domain-specific knowledge and constraints
- Approach guarantees the structure of generated text, suitable for creating reliable interfaces
- Minimal overhead compared to existing solutions while outperforming them
- Implementation provided in the open-source Python library Outlines
- Framework has various applications, including generating sequences of tokens with specific formatting requirements
- Promising technique leveraging finite-state machines, regular expressions, and context-free grammars for improving neural text generation

## Summary

In the article titled "Efficient Guided Generation for Large Language Models," authors Brandon T. Willard and Rémi Louf present a novel approach to the problem of neural text generation. They propose reformulating the problem in terms of transitions between states in a finite-state machine, which allows for efficient text generation guided by regular expressions and context-free grammars. This approach enables the construction of an index over a language model's vocabulary, making it model agnostic and allowing for the enforcement of domain-specific knowledge and constraints. The authors highlight that their approach also guarantees the structure of the generated text, making it suitable for creating reliable interfaces. They emphasize that their method adds minimal overhead to the token sequence generation process while outperforming existing solutions. The article provides an implementation of their approach in the open-source Python library Outlines. The authors conclude by stating that their framework has various applications, including generating sequences of tokens from large language models that adhere to specific formatting requirements. Overall, this article introduces a promising technique for improving neural text generation by leveraging finite-state machines, regular expressions, and context-free grammars. The proposed method offers efficiency, flexibility, and reliability in generating structured text outputs from language models.