A Base Language Learning Model (Base LLM) represents the fundamental model obtained after initial AI training on a broad dataset. For instance, models like GPT-3, GPT-4, or BERT fall under this category. They are trained on large volumes of internet text to understand and predict language patterns. However, they don't inherently follow instructions provided in the prompt.
Take the case of a digital marketing firm that uses a Base LLM for generating content. The model could generate a high-quality article on a given topic, but it might not strictly adhere to specific instructions, like maintaining a particular tone or including certain keywords.
An Instruction Tuned Language Learning Model (Instruction Tuned LLM), on the other hand, undergoes an additional round of training on a narrower dataset, specifically designed to fine-tune its performance. This secondary training aims to enable the model to better understand and respond to specific instructions provided in the prompt.
Let's revisit the digital marketing firm example. With an Instruction Tuned LLM, if the firm asks the model to write an article on a given topic while maintaining a playful tone and including specific keywords, the model will be much more likely to follow these instructions closely. This enhanced responsiveness to instructions can lead to more accurate, efficient, and satisfactory outcomes for businesses.
While both Base LLMs and Instruction Tuned LLMs have their merits, the choice between them boils down to the specific needs and expectations of your organization. A Base LLM offers broad language understanding and prediction capabilities. In contrast, an Instruction Tuned LLM provides a more refined, instruction-driven performance, helping you achieve specific, tailored results with your AI model.