One of the earliest forms of Linguistic AI is machine translation (MT), technology which automatically translates text or speech from one language to another. Since it was pioneered in the 1950’s, MT has undergone a significant transformation, delivering unprecedented quality throughout each evolution. The current form is Neural Machine Translation (NMT), which has been embedded into translation tools and leveraged by organizations across the world.

A pivotal development in NMT was the introduction of the transformer which allowed MT systems to capture complex word dependencies more effectively, leading to more fluent and accurate translations. As researchers expanded the size of transformer models, increasing the number of layers, parameters, and the amount of data used for training, models were able to understand and generate human-like text across a wide range of tasks, not just translation. Thus, the large language model (LLM) was born – examples include OpenAI’s GPT (Generative Pre-trained Transformer) and LaMDA (Language Model for Dialogue Applications), which can perform tasks like text completion, question answering, and creative writing with high accuracy. LLMs have now become the foundation for many cutting-edge AI applications, transforming industries and delivering far more than just translation capabilities.

To further enhance LLM output, organizations can opt to train or fine-tune general-purpose models in line with their industry and use case to make the models more effective and efficient. Larger companies with the necessary resources and expertise might choose to train or fine-tune LLMs in-house, so they have complete control over the process. Other companies without the internal infrastructure or expertise needed may choose to partner with an AI service provider that offers AI training and fine-tuning data services. These providers can collect and label domain-specific data, or the company’s proprietary data, to train the LLM and help fine-tune it using human-in-the-loop techniques (such as prompt engineering, reinforcement learning from human feedback (RLHF), and red teaming) to the company’s specific needs.