SynthLang is a hyper-efficient prompt language designed to optimize interactions with Large Language Models (LLMs) like GPT-4o by leveraging logographical scripts and symbolic constructs. By compressing complex instructions into fewer tokens (reducing token usage by 40–70%), SynthLang significantly lowers inference latency, making it ideal for latency-sensitive applications such as high-frequency trading, real-time analytics, and compliance checks.
Additionally, SynthLang mitigates English-centric biases in multilingual models, enhancing information density and ensuring more equitable performance across diverse languages. Its scalable design maintains or improves task performance in translation, summarization, and question-answering, fostering faster, fairer, and more efficient AI-driven solutions.
Large Language Models (LLMs) such as GPT-4o and Llama-2 exhibit English-dominant biases in intermediate embeddings, leading to inefficient and oft
