Label-Free Evaluation of Retrieval-Augmented Generation in a University Policy Chatbot

Abstract

Retrieval-augmented generation (RAG) is a common approach for building chatbots that combine fluent language generation with access to domain-specific knowledge. However, these systems often produce answers that are plausible but weakly supported by retrieved documents or that cite irrelevant sources. Evaluating RAG chatbots is difficult because it requires measuring both how well answers address user queries and how well they are grounded in retrieved content.

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