NLG encompasses a broader range of tasks, including generating coherent text, summaries, and more from given prompts.
Natural Language Generation (NLG) focuses on creating coherent and contextually relevant text and encompasses tasks such as:
- Text Completion: Generating the continuation of a given text.
- Summarization: Producing concise summaries of longer content.
- Paraphrasing: Restating content in a different way.
- Dialogue Generation: Creating responses for chatbots and conversational agents.
- Content Generation: Crafting creative or informative text based on prompts.
- Data-to-Text Generation: Translating structured data (e.g., tables or graphs) into natural language.
NLG enables applications like chatbots, automated report writing, and content generation tools.
Casual Language Modeling (CLM) is a technique that focuses on predicting the next word in a sequence based on previous words, which is often used in Natural Language Generation (NLG) tasks. So, while CLM and NLG they are related, they are not synonymous; CLM is a specific approach used for certain types of NLG applications. In other words, CLM is a method within NLG.