What do developers use instead of creating their own large language models for natural language processing?

Developers often opt for pre-built models for natural language processing tasks because these models have been developed and fine-tuned on extensive datasets by experts in the field. Pre-built models offer a significant advantage in terms of time and resources, as they eliminate the need for developers to train their own models from scratch, which can be costly and time-consuming. These models typically come with built-in capabilities for understanding and generating human language, making them suitable for various applications such as sentiment analysis, text classification, and chatbots.

By utilizing pre-built models, developers can leverage the latest advancements in natural language processing without needing deep expertise in the underlying algorithms or massive computational power for training. This allows them to focus on integrating these capabilities into their applications, thereby increasing efficiency and accelerating development cycles.