Language is not static; it is a living, evolving organism. ChinglishAI introduces a novel approach to Natural Language Generation (NLG) that prioritizes "memetic fitness" over grammatical correctness. By leveraging Large Language Models (LLMs) fine-tuned on a proprietary dataset of high-viral Chinglish phrases, we simulate the natural selection process of language evolution. This paper outlines our three-engine architecture: The Creation Engine (Mutation), The Evolution Engine (Selection), and The Analysis Engine (Cultural Decoding).
Responsible for generating linguistic variants. Unlike traditional translation models that minimize error, this engine maximizes "semantic tension"—the gap between literal translation and intended meaning.
Acts as the environment filter. It predicts the "virality potential" of generated phrases based on social media trends, shortness, and shock value.
Deconstructs the cultural context behind Chinglish phrases, explaining *why* a phrase is funny or poignant to both native English speakers and Chinese speakers.
Our model is trained on a curated dataset of 50,000+ verified Chinglish instances, collected from signage, menus, subtitles, and social media comments. Each instance is labeled with:
By utilizing Reinforcement Learning from Human Feedback (RLHF), the model learns to favor outputs that trigger a "humor response" or "insight response" in human evaluators, rather than outputs that are merely grammatically correct.