Probabilistic Consensus through Ensemble Validation: A Framework for LLM Reliability

Understand

Large Language Models (LLMs) have shown significant advances in text generation but often lack the reliability needed for autonomous deployment in high-stakes domains like healthcare, law, and finance.

• Existing approaches rely on external knowledge or human oversight, limiting scalability.
• We introduce a novel framework that repurposes ensemble methods for content validation through model consensus.
• In tests across 78 complex cases requiring factual accuracy and causal consistency, our framework improved precision from 73.1% to 93.9% with two models (95% CI: 83.5%-97.9%) and to 95.6% with three models (95% CI: 85.2%-98.8%).