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.
- 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 "θζ",{[language="en"][000x01]}
for "apple").
- Reduce token quantity to lower computational costs in LLM training.
- Enhance multilingual NLP performance by introducing unified token representations.
- Improve efficiency in long-text processing for AI models.
- Develop an encoding system to map natural language text to HL tokens.
- Apply compression algorithms like BPE or entropy-based encoding.
- Train models using HL tokenized datasets to measure efficiency improvements.
- Compare with traditional token-based training (baseline: GPT/BERT).
- Ensure HL can accurately decode back to NLP content.
- Use attention-based neural networks for reconstruction validation.
β
Lower computational cost by reducing token count.
β
Improved language model efficiency, especially in multilingual settings.
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Potential advancements in AI-driven knowledge representation.
We welcome contributions from researchers and developers in NLP and AI. Feel free to open issues, discuss improvements, or submit PRs.
This project is open-source under the MIT License.
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