9 docs tagged with "synthetic-data"

Systematic approaches to filtering synthetic data for quality, diversity, safety, and alignment - the layered pipeline that separates fine-tuned models that work from models that regress.

Building distillation datasets: capturing frontier model knowledge, reasoning traces, and calibration into training data for smaller, efficient models - from Orca to Phi.

Evol-Instruct: systematically evolving instruction datasets to create complex, diverse training data that produces stronger instruction-following models - the technique behind WizardLM and WizardCoder.

Use frontier LLMs to generate high-quality instruction-following, reasoning, and preference datasets - sampling strategies, diversity maximization, and quality vs. quantity tradeoffs.

Learn to generate, filter, and use synthetic training data at scale - from Self-Instruct bootstrapping to Evol-Instruct complexity evolution, distillation datasets, and RAG evaluation corpora.

Copyright exposure, memorization risks, differential privacy, bias auditing, terms-of-service compliance, and the governance processes required for defensible synthetic data pipelines.

How the Self-Instruct paper bootstrapped instruction-following datasets from a tiny seed set using GPT-3, enabling the Alpaca era of aligned models - and how to implement it today.

Generating question-answer pairs, evaluation datasets, and retrieval test cases from documents to build, evaluate, and systematically improve RAG systems.

Understand why synthetic data has become central to AI engineering - the labeled data bottleneck, privacy constraints, rare events, LLMs as generators, landmark case studies, and when synthetic beats real.