In recent years, machine translation has come a long way. Thanks to advances in artificial intelligence and natural language processing (NLP), it’s now possible to translate text from one language to another quickly and accurately. However, traditional approaches to machine translation have their limitations. They often rely on rule-based systems or statistical models that can struggle with complex sentence structures and idiomatic expressions.
That’s where generative large language models (LLMs) come in.
Suggesting that deep learning models based are capable of generating realistic text from a prompt would be an understatement. Ever since the advent of Transformer models, natural language processing has been undergoing a revolution. Large language models (LLMs), and generative models in general, have received public attention with the releases of text-to-image models (Stable Diffusion) and of course the ChatGPT chatbot. While LLMs have impressive generalized capabilities for text generation, they can be challenging to use due to their size (hundreds of millions or even billions of trainable parameters).