Don’t be data poor — Anuj Iravane, Anterior
The talk addresses the critical problem of 'data poverty' in highly regulated domains like healthcare, where the most valuable data (Patient Health Information or PHI) is ephemeral and legally prohibited from being retained, anonymized, or derived for dataset creation. The core solution presented is synthetic data generation. This process involves reversing the standard inference workflow—starting by sampling a desired label and reasoning trace, and then generating the necessary unstructured medical record that would have produced it. The resulting pipeline uses an LLM-based, coarse-to-fine approach, ensuring high fidelity while maintaining domain expert control.
Key takeaways
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Reverse Inference for Data Generation
5:20
Instead of running the forward task (Unstructured Data + Policy $\rightarrow$ Label), the method reverses this by sampling a label and a reasoning trace first, then generating the input data that supports it. This circumvents the diversity problem inherent in standard LLM generation.
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Domain Expert Ownership (Human-in-the-Loop)
11:30
To ensure generated data is useful, domain experts (clinicians) must own the pipeline. This is achieved by enabling them to interject at any point in generation and modeling the entire workflow as a skills-based system running on an agent harness.
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Synthetic Data Fidelity
14:35
The generated data can be highly accurate, with early results showing that in a blind review, clinicians were only able to distinguish synthetic from real records about 60% of the time.