Using multi-model cross-examination to pinpoint fabrications and isolate truth vectors.
All large language models are subject to "hallucinations"—statistically plausible but completely fabricated assertions. When a user queries a single model, identifying these hallucinations is extremely difficult without external search tools.
By deploying the multiplex approach, a user can broadcast a prompt across independent weights trained on distinct datasets (such as Claude, GPT-4, and Local Llama 3). If three out of four models output identical facts, while the fourth asserts an outlier, the outlier can immediately be flagged as a high-probability fabrication. Cross-examination becomes your shield.