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🌍 Language

Hyper Language (HL) - A High-Density NLP Representation

πŸ“Œ Introduction

Hyper Language (HL) is a conceptual framework aimed at improving the efficiency of large language model (LLM) training by reducing token count while maintaining semantic integrity. This approach leverages semantic compression, syntactic optimization, and cross-language tagging to create a high-density text representation.

πŸ” Key Features

  • Semantic Compression: Frequently used words and phrases are represented using minimal tokens to reduce redundancy.
  • Syntactic Optimization: Simplifies sentence structures while preserving the meaning, enabling faster parsing.
  • Accurate Decoding: HL ensures that compressed tokens can be fully restored to natural language, preventing semantic loss.
  • Cross-Language Tagging: Enables unified representations of synonymous words across multiple languages (e.g., {[language="cn"][000x01]} for "θ˜‹ζžœ", {[language="en"][000x01]} for "apple").

🎯 Research Goals

  1. Reduce token quantity to lower computational costs in LLM training.
  2. Enhance multilingual NLP performance by introducing unified token representations.
  3. Improve efficiency in long-text processing for AI models.

βš™οΈ Implementation Plan

1️⃣ HL Token Encoding

  • Develop an encoding system to map natural language text to HL tokens.
  • Apply compression algorithms like BPE or entropy-based encoding.

2️⃣ Transformer-Based Training

  • Train models using HL tokenized datasets to measure efficiency improvements.
  • Compare with traditional token-based training (baseline: GPT/BERT).

3️⃣ Decoding & Semantic Restoration

  • Ensure HL can accurately decode back to NLP content.
  • Use attention-based neural networks for reconstruction validation.

πŸ“Š Expected Impact

βœ… Lower computational cost by reducing token count.
βœ… Improved language model efficiency, especially in multilingual settings.
βœ… Potential advancements in AI-driven knowledge representation.

πŸ“ Get Involved

We welcome contributions from researchers and developers in NLP and AI. Feel free to open issues, discuss improvements, or submit PRs.

πŸ”— License

This project is open-source under the MIT License.


πŸ“’ Have insights or suggestions? Join the conversation by opening a GitHub Issue or contributing to the repository!

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