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 in a conversational system without explicit control. This raises a research question about such a necessity. In this study%2C we propose to investigate the need for each turn of system response to be augmented with external knowledge. In particular%2C by leveraging human judgements on the binary choice of adaptive augmentation%2C we develop RAGate%2C a gating model%2C which models conversation context and relevant inputs to predict if a conversational system requires RAG for improved responses. We conduct extensive experiments on devising and applying RAGate to conversational models and well-rounded analyses of different conversational scenarios. Our experimental results and analysis indicate the effective application of RAGate in RAG-based conversational systems in identifying system responses for appropriate RAG with high-quality responses and a high generation confidence. This study also identifies the correlation between the generation's confidence level and the relevance of the augmented knowledge.?quality=80&w=800)
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