Meaning Typed Prompting: A Technique for Efficient, Reliable Structured Output Generation

TL;DR

Meaning Typed Prompting (MTP) is a novel technique that improves the accuracy, reliability, and efficiency of structured output generation in large language models by integrating types and abstractions into prompts.

Contribution

The paper introduces MTP, a new prompting method that enhances structured output quality and efficiency by incorporating expressive type definitions and abstractions.

Findings

MTP outperforms existing methods in accuracy and reliability.

MTP reduces token usage and computational overhead.

Empirical results show improved structured data generation.

Abstract

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.