There is no such thing as a Trained LLM

Traditional LLMs are often trained on tasks that don't align with their actual use cases, leading to inefficiencies and unnecessary complexity. The notion that training is essential for LLMs is challenged, suggesting that unsupervised learning and specialized architectures might provide better results. Various evaluation metrics and overlooked criteria like exhaustivity, inference, and ease of use are discussed. The article introduces xLLM, a next-gen architecture that emphasizes efficiency and user-friendly features, potentially eliminating the need for extensive training.