Real-Time Evaluation Models for RAG: Who Detects Hallucinations Best?

Ashish Sardana

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arXiv · 2503.21157

Real-Time Evaluation Models for RAG: Who Detects Hallucinations Best?

Ashish Sardana

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Abstract

This article surveys Evaluation models to automatically detect hallucinations in Retrieval-Augmented Generation (RAG), and presents a comprehensive benchmark of their performance across six RAG applications. Methods included in our study include: LLM-as-a-Judge, Prometheus, Lynx, the Hughes Hallucination Evaluation Model (HHEM), and the Trustworthy Language Model (TLM). These approaches are all reference-free, requiring no ground-truth answers/labels to catch incorrect LLM responses. Our study reveals that, across diverse RAG applications, some of these approaches consistently detect incorrect RAG responses with high precision/recall.

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Ashish Sardana. 2025-03-27. Real-Time Evaluation Models for RAG: Who Detects Hallucinations Best?. https://arxiv.org/abs/2503.21157

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