SHAP, XGBoost, and the End of Fake References: Inside a 5-Layer Predictive Citation System

Author(s): R. Thompson (PhD)

Originally published on Towards AI.

Measuring and predicting citation hallucination with real-world data pipelines 🔍

When generative AI tools like Perplexity’s Deep Research or Elicit AI output beautifully structured academic references, there’s a hidden risk: those citations might not exist. Users often assume that references are real because the system looks reliable.

This article discusses the challenges of citation hallucination in generative AI, presenting a predictive citation validation system that combines various architectural layers for real-time trust assessment. It highlights the importance of active learning in classifying citation reliability and offers a case study demonstrating the effective reduction of citation review time through the implementation of this predictive model.

Read the full blog for free on Medium.

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Published via Towards AI