Prompt Pipelines is a concept in natural language processing (NLP) that involves the creation of a series of steps or ‘pipelines’ to process and generate language. These pipelines use machine learning models to analyse text, understand its meaning, and generate appropriate responses. The process begins with tokenisation, where the input text is broken down into smaller parts or ‘tokens’.
These tokens are then passed through a series of models, each with its own specific purpose. For instance, the Named Entity Recognition (NER) model identifies and classifies entities in the text, such as people, places, or organisations. The Part-of-Speech (POS) tagging model determines the grammatical role of each token, while the dependency parsing model analyses the grammatical structure of the sentence.
Finally, the Coreference Resolution model identifies when different words refer to the same entity, ensuring consistency in the generated response. This pipeline process allows for more nuanced and accurate language generation, making it a crucial tool in the development of chatbots and virtual assistants. It’s a complex but essential aspect of NLP, and understanding it can significantly improve the effectiveness of language-based AI systems.
Go to source article: https://cobusgreyling.medium.com/prompt-pipelines-de48e25de224