Motivations for developing and using LLMs

The motivation to develop and use LLMs arises from several factors related to the capabilities of these models, and the potential benefits they can bring in diverse applications. The following subsections detail a few of these key motivations.

Improved performance

LLMs, when trained with sufficient data, generally demonstrate better performance compared to smaller models. They are more capable of understanding context, identifying nuances, and generating coherent and contextually relevant responses. This performance gain applies to a wide range of tasks in NLP, including text classification, named entity recognition, sentiment analysis, machine translation, question answering, and text generation. As shown in Table 7.1, the performance of BERT – one of the first well-known LLMs – and GPT is compared to the previous models on the General Language Understanding Evaluation (GLUE) benchmark. The GLUE benchmark is a collection...