Self-improving data synthesis can scale embedding models across domains and tasks

arXiv:2610.14730v1 Announce Type: cross Abstract: Adapting embedding models to new tasks requires training data that reflect task-specific information needs and relevance criteria. However, manual data design is costly and difficult to scale. Here we introduce SYNTRA, a novel retrieval data synthesis method whose self-improvement loop iteratively refines reusable synthesis instructions using feedback on trial outputs. This process requires only an unlabelled corpus and a few relevant query-document pairs. Experiments across 24 retrieval datasets spanning 15 domains demonstrate the effectivenes

This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.