How does generative AI extend NLP capabilities?

Generative AI extends NLP capabilities by enabling the creation of new content, which includes tasks such as writing essays and holding conversations. Unlike traditional NLP approaches that may focus on understanding and manipulating existing text, generative AI models have the ability to produce unique outputs based on learned patterns in data. This means they can generate text that is coherent and contextually relevant without being confined to pre-existing material. This capability allows for a broad range of applications, including chatbots for conversational interactions, content creation for articles or posts, and even creative writing, which enhances the utility and versatility of NLP technologies in various fields.

The other options reflect more traditional aspects of NLP or limitations that do not align with the innovative nature of generative AI. For instance, enhancing keyword extraction is important for search engine optimization but does not involve generating new content. Streamlining data collection methods is beneficial for model training but does not directly relate to the generative capabilities of AI. Limiting content generation to predefined templates contrasts sharply with the essence of generative AI, which thrives on creativity and flexibility rather than restrictions.