In-Context Learning for Scalable and Online Hallucination Detection in RAGS
Nicolò Cosimo Albanese · 2024
Ensuring fidelity to source documents is crucial for the responsible use of Large Language Models (LLMs) in Retrieval Augmented Generation (RAG) systems. We propose a lightweight method for real-time hallucination detection, with potential to be deployed as a model-agnostic microservice to bolster reliability. Using in-context learning, our approach evaluates response factuality at the sentence level without annotated data, promoting transparency and user trust. Compared to other prompt-based and semantic similarity baselines from recent literature, our method improves hallucination detection F1 scores by at least 11%, with consistent performance across different models. This research offers a practical solution for real-time validation of response accuracy in RAG systems, fostering responsible adoption, especially in critical domains where document fidelity is paramount.