Guiding Large Language Models to Generate Computer-Parsable Content

Understand

We propose a method to guide Large Language Models (LLMs) in generating structured content adhering to specific conventions without fine-tuning.

• By utilizing coroutine-based content generation constraints through a pre-agreed context-free grammar (CFG), LLMs are directed during decoding to produce formal language compliant outputs.
• This enhances stability and consistency in generating target data structures, types, or instructions, reducing application development complexities.
• Experimentally, error rates of GPT-2 and Gemma exceed 95% for DSLs longer than 36 and 282 tokens, respectively.