LingVarBench

The Privacy Challenge

Medical NLP is hindered by the lack of publicly available data due to stringent privacy regulations (HIPAA). LingVarBench addresses this by generating synthetic patient-doctor conversations that are indistinguishable from real ones but contain zero real patient data.

Architecture

The system uses a two-stage process:

1. Generation Pipeline: A DSPy module creates a conversation based on a medical topic, injecting specific entities (diseases, medications) while ensuring natural flow.
2. SIMBA Optimizer: The Stochastic Introspective Mini-Batch Ascent optimizer evolves the prompt itself to maximize linguistic diversity and medical accuracy.

SIMBA Optimization

SIMBA treats the prompt as an organism in an evolutionary algorithm.

class SIMBAOptimizer:
    def optimize_prompt(self, base_prompt, evaluation_data):
        population = self._initialize_population(base_prompt)
        for generation in range(self.generations):
            # Evaluate -> Select -> Mutate -> Repeat
            fitness_scores = [self._evaluate(p) for p in population]
            population = self._create_next_generation(population, fitness_scores)
        return best_prompt

Real-World Impact

Models trained purely on LingVarBench synthetic data achieved >90% accuracy when tested on real, private healthcare datasets. This "synthetic-to-real" transfer learning proves that high-quality synthetic data can effectively substitute for sensitive real-world data.