GenLang

Self-Decoding Compression Architecture

GenLang: Self-Decoding Compression Architecture

Overview

GenLang offers a novel approach to managing token constraints in large language models (LLMs) through self-decoding compression. By embedding decoding instructions within compressed text, GenLang reduces token usage by approximately 50%, enabling richer interactions with lower API costs.

This repository provides the GenLang research paper, implementation examples, and scripts for compression and decompression to help developers and researchers integrate GenLang with LLMs.

Research Paper

Download the GenLang Paper

Key Features

• Token Reduction: Compress input data while retaining essential context.
• Cost Savings: Lower API usage costs up to 50%.
• Interoperability: Standardized format to support multi-model data exchange.
• Preservation of Detail: High interpretation accuracy compared to traditional summarization.

Repository Contents

• GenLang_Paper.pdf: The full research paper detailing GenLang's methodology, benefits, and case studies.
• examples/: Python scripts for implementing GenLang in specific case studies.
• scripts/: Core Python scripts for compression, decompression, and performance evaluation.
• docs/: Additional documentation, including an implementation guide and performance metrics.
• resources/: Further resources, references, and summaries of case studies.

Getting Started

Prerequisites

• Python 3.8+
• OpenAI API access for testing with LLMs
• Necessary packages: requests, numpy, pandas

Installation

1. Clone this repository:

   git clone https://github.com/yourusername/GenLang-Self-Decoding-Compression.git
   cd GenLang-Self-Decoding-Compression