Text Synthetic Data generation
Synthetic data generation for text creates high-quality artificial text datasets that mimic the properties and patterns of original text data, playing a crucial role in Generative AI applications. This technique enhances the performance of large language models (LLMs) by providing extensive training datasets, which improve model accuracy and robustness. It addresses data scarcity by generating text for specialized domains or languages where data is limited. Additionally, synthetic text generation ensures privacy preservation, allowing organizations to create useful datasets without compromising sensitive information, thereby complying with data privacy regulations while enabling comprehensive data analysis and model training
Key Capabilities
- Privacy preserving RAG: Use RAG without ever exporing PII information
- Data augmentation: Generate more data points to feed your LLMs without the effort of real world data colletion, which is expensive and time consuming.
- Data Balancing: Generate more rare scenarios so your LLM can improve it's anser accuracy and reduce hallucinations.
Feature in Beta
This feature is in beta. Contact us if you are having issues!
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