Domain-Specific Shorthand for Generation Based on Context-Free Grammar

TL;DR

This paper introduces a domain-specific shorthand format based on context-free grammar to reduce token usage in structured data generation by large language models, significantly lowering latency and costs.

Contribution

It presents a novel CFG-based shorthand method that unambiguously compresses structured data, improving efficiency in GenAI applications.

Findings

Achieves 3x to 5x token reduction in data generation

Reduces latency and operational costs substantially

Provides scalable solution for structured data in LLMs

Abstract

The generation of structured data in formats such as JSON, YAML and XML is a critical task in Generative AI (GenAI) applications. These formats, while widely used, contain many redundant constructs that lead to inflated token usage. This inefficiency is particularly evident when employing large language models (LLMs) like GPT-4, where generating extensive structured data incurs increased latency and operational costs. We introduce a domain-specific shorthand (DSS) format, underpinned by a context-free grammar (CFG), and demonstrate its usage to reduce the number of tokens required for structured data generation. The method involves creating a shorthand notation that captures essential elements of the output schema with fewer tokens, ensuring it can be unambiguously converted to and from its verbose form. It employs a CFG to facilitate efficient shorthand generation by the LLM, and to create parsers to translate the shorthand back into standard structured formats. The application of our approach to data visualization with LLMs demonstrates a significant (3x to 5x) reduction in generated tokens, leading to significantly lower latency and cost. This paper outlines the development of the DSS and the accompanying CFG, and the implications of this approach for GenAI applications, presenting a scalable solution to the token inefficiency problem in structured data generation.