Efficient Guided Generation for Large Language Models (2307.09702v4)

Abstract: In this article we show how the problem of neural text generation can be constructively reformulated in terms of transitions between the states of a finite-state machine. This framework leads to an efficient approach to guiding text generation with regular expressions and context-free grammars by allowing the construction of an index over a LLM's vocabulary. The approach is model agnostic, allows one to enforce domain-specific knowledge and constraints, and enables the construction of reliable interfaces by guaranteeing the structure of the generated text. It adds little overhead to the token sequence generation process and significantly outperforms existing solutions. An implementation is provided in the open source Python library Outlines

• The paper introduces an FSM-based indexing method that reduces per-token computation to O(1) for guided text generation.
• It reformulates guided generation as an FSM transition problem to efficiently enforce regular expression and CFG constraints.
• Experimental comparisons show improved scalability and faster response times in structured output tasks like JSON, Python, and SQL.

Efficient Guided Generation for LLMs

The paper "Efficient Guided Generation for LLMs" by Brandon T. Willard and Rémi Louf introduces a novel approach for guided text generation using LLMs. The authors reformulate the neural text generation process by leveraging finite-state machines (FSMs) to efficiently guide text generation according to regular expressions and context-free grammars (CFGs). This model-agnostic technique promises to enforce domain-specific constraints while maintaining computational efficiency.

Key Contributions

The paper's primary contribution lies in its innovative use of FSMs to construct an index over a LLM's vocabulary. This indexing allows for efficient computation of token probabilities that adhere to specific grammatical or structural constraints. This approach, implemented in the open-source Python library Outlines, significantly reduces computational overhead compared to traditional methods.

Methodology

The authors redefine guided text generation as a transition problem within FSMs. This framework allows starting and stopping the guided generation efficiently, making it feasible to ensure the output conforms to predefined structures such as JSON, Python, or SQL formats. The paper claims that their methodology achieves an average computational cost of $\mathcal{O}(1)$ per token, a substantial improvement over the current $\mathcal{O}(N)$ costs, where $N$ is the vocabulary size.

The method involves constructing a finite automaton for the guiding regular expressions, enabling the pre-computation of indices that map FSM states to permissible tokens. This pre-computed mapping mitigates the need for runtime vocabulary evaluation, a common bottleneck in existing methods.

Numerical Results and Comparisons

The authors present experimental comparisons with existing solutions, including the Guidance library. Their implementation demonstrates improved scalability and performance across various scenarios, as evidenced by reduced response times when increasing the maximum sampled tokens. The FSM indexing method's efficiency is particularly notable when handling regular expression-guided generation tasks.

Implications and Future Directions

Practically, this research implies that LLMs can be more effectively integrated into systems requiring structured output, like programming language interpreters or data management tools, without extensive fine-tuning. Theoretically, it opens new avenues for exploring how LLMs handle syntactic knowledge and constraints.

The authors also discuss potential extensions of their work to iterative parsing with pushdown automata, suggesting that their indexing approach could extend beyond regular expressions to CFGs. This capability could benefit applications requiring complex syntactic structural compliance, further enhancing the utility of LLMs in domain-specific tasks.

Moving forward, the authors propose additional research directions, such as integrating guided generation techniques into LLM training processes. By aligning training objectives with syntactic constraints, models may achieve better generalization with less training data.

Overall, the paper presents a well-conceived methodology to improve guided generation in LLMs, offering practical enhancements and paving the way for future research inquiries in efficient model guidance and training techniques.