Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval
Ingeol Baek, Hwan Chang, ByeongJeong Kim, Jimin Lee, Hwanhee Lee · 2025
Retrieval-Augmented Generation (RAG) enhances language models by retrieving and incorporating relevant external knowledge.However, traditional retrieve-and-generate processes may not be optimized for real-world scenarios, where queries might require multiple retrieval steps or none at all.In this paper, we propose a Probing-RAG, which utilizes the hidden state representations from the intermediate layers of language models to adaptively determine the necessity of additional retrievals for a given query.By employing a pre-trained prober, Probing-RAG effectively captures the model's internal cognition, enabling reliable decisionmaking about retrieving external documents.Experimental results across five open-domain QA datasets demonstrate that Probing-RAG outperforms previous methods while reducing the number of redundant retrieval steps.