Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval

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 decision-making 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.

@article{baek2025_2410.13339,
  title={ Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval },
  author={ Ingeol Baek and Hwan Chang and Byeongjeong Kim and Jimin Lee and Hwanhee Lee },
  journal={arXiv preprint arXiv:2410.13339},
  year={ 2025 }
}