Synthetics
Sample-to-Dataset: Generate Rich Datasets from Limited Samples Using Data Designer
Seed to succeed: use the sample-to-dataset workflow to create diverse, large-scale synthetic datasets tailored to your needs with nothing but a few samples.
Read more...Teaching AI to Think: A New Approach with Synthetic Data and Reflection
Gretel's synthetic GSM8k dataset shows an 84% improvement for AI Reasoning tasks vs synthetic data generated without the Reflection technique.
Read more...Generate Differentially Private Synthetic Text with Gretel GPT
Safely leverage sensitive or proprietary text data for advanced language model training and fine-tuning
Read more...Synthetic Data and the Data-centric Machine Learning Life Cycle
Gretel's synthetic data platform overcomes challenges across the data-centric machine learning life cycle to enable AI and ML solutions.
Read more...Red Teaming Synthetic Data Models
How we implemented a practical attack on a synthetic data model to validate its ability to protect sensitive information under different parameter settings.
Read more...Fine-tuning Models for Healthcare via Differentially-Private Synthetic Text
How to safely fine-tune LLMs on sensitive medical text for healthcare AI applications using Gretel and Amazon Bedrock
Read more...Introducing world's largest synthetic open-source Text-to-SQL dataset
Gretel releases largest open source Text-to-SQL dataset to accelerate AI model training
Read more...An Awesome Synthetic Multilingual Prompts Dataset
Gretel's latest open synthetic dataset aims to enhance LLM interactions and contributes to the popular 'awesome-chatGPT-prompts' GitHub repository.
Read more...Synthesizing Private Patient Data with Gretel: A Step-by-Step Guide
Create privacy-safe synthetic patient data with Gretel, ensuring compliance, secure sharing, and actionable insights for AI and machine learning in healthcare.
Read more...GSM-Symbolic: Analyzing LLM Limitations in Mathematical Reasoning and Potential Solutions
What The Recent Paper on LLM Reasoning Got Right—And What It Missed.
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