Overview: Natural Language Generation via GPT-3, ChatGPT and Data-to-Text
Both, GPT and Data-to-Text are NLG technologies. NLG means "Natural Language Generation" and refers to the automated generation of natural language text, even though there are significant differences.
With data-to-text software like AX Semantics, you can generate high-quality content based on structured data. The user has control over the text results, can intervene in the text creation at any time, and make updates or adjustments. This control ensures text consistency, meaningfulness and quality. And: By working with a central rule set instead of generating each individual text, content creation becomes a scalable process. Tools based on structured data can, for example, create hundreds or thousands of different product descriptions with variable details in just a few moments.
GPT stands for "Generative Pre-trained Transformer". It is a language model that learns from existing text and can provide different ways to end a sentence. It has been trained with hundreds of billions of words, representing a significant portion of the internet. Unlike data-to-text, GPT can only be used to generate individual texts, although this can be done quickly. However, the user has not that much control over the generated content and tonality and needs to review every text.
With data-to-text software like AX Semantics, you can generate high-quality content based on structured data. The user has control over the text results, can intervene in the text creation at any time, and make updates or adjustments. This control ensures text consistency, meaningfulness and quality. And: By working with a central rule set instead of generating each individual text, content creation becomes a scalable process. Tools based on structured data can, for example, create hundreds or thousands of different product descriptions with variable details in just a few moments.
GPT stands for "Generative Pre-trained Transformer". It is a language model that learns from existing text and can provide different ways to end a sentence. It has been trained with hundreds of billions of words, representing a significant portion of the internet. Unlike data-to-text, GPT can only be used to generate individual texts, although this can be done quickly. However, the user has not that much control over the generated content and tonality and needs to review every text.
Which Technology is suitable for which Use Cases?
You can easily find out which technology is better for your use case. In the following document, we have summarized the criteria you can use to decide which technology suits your business best. Just click on the image to open the PDF file!
How does Content Generation with Data-to-Text Software work?
1. Upload Your Data
Upload structured data like product features and information from a PIM system or another source to form the information base for your content.
2. Configure Your Content
Implement your desired statements and rules to define how your uploaded data should appear and be interpreted in the texts.
3. Generate Unique Content
Generate thousands of individual texts for all your data records at the touch of a button. You can update and refresh your content as often as you like.
The content team of the online price comparison portal billiger.de generates over 100,000 unique and high-quality SEO product descriptions per year with the AX Semantics content generation software.
Without automated content generation, it would be impossible to write descriptions for the huge product range of billiger.de.
- Bernd Vermaaten, CEO of the brand billiger.de