Meaning Typed Prompting: A Technique for Efficient, Reliable Structured Output Generation
This addresses the issue of rigid JSON schemas limiting LLMs' adaptability for complex tasks, offering a more efficient and reliable method for developers and researchers working with structured data generation.
The paper tackles the problem of unreliable structured output generation in Large Language Models (LLMs) by introducing Meaning Typed Prompting (MTP), which integrates types and abstractions into prompting to improve clarity and efficiency, resulting in outperforming existing frameworks in accuracy, reliability, consistency, and token efficiency on multiple benchmarks.
Extending Large Language Models (LLMs) to advanced applications requires reliable structured output generation. Existing methods which often rely on rigid JSON schemas, can lead to unreliable outputs, diminished reasoning capabilities, and increased computational overhead, limiting LLMs' adaptability for complex tasks. We introduce Meaning Typed Prompting (MTP), a technique for efficient structured output generation that integrates types, meanings, and abstractions, such as variables and classes, into the prompting process. By utilizing expressive type definitions, MTP enhances output clarity and reduces dependence on complex abstractions, simplifying development, and improving implementation efficiency. This enables LLMs to understand relationships and generate structured data more effectively. Empirical evaluations on multiple benchmarks demonstrate that MTP outperforms existing frameworks in accuracy, reliability, consistency, and token efficiency. We present Semantix, a framework that implements MTP, providing practical insights into its application.