Introduction
In Retrieval-Augmented Generation (RAG) systems, language models generate responses by combining retrieved documents with their pre-trained knowledge. This hybrid approach aims to improve the relevance and accuracy of generated content by grounding it in real data. However, even in RAG settings, hallucinations—instances where the model produces information not supported by the retrieved sources—can still occur. Evaluating and mitigating these hallucinations is crucial for ensuring the reliability of the generated outputs.
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Importance of Hallucination Evaluation
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Hallucination evaluation is a critical component in the effective deployment of RAG-based systems. By categorizing responses into Low, Medium, and High hallucination levels, the system can provide users with valuable insights into the reliability of the generated content. This evaluation process not only enhances user confidence but also supports the responsible use of AI in contexts where accuracy is crucial.
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