1. Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval
- Author
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Baek, Ingeol, Chang, Hwan, Kim, Byeongjeong, Lee, Jimin, and Lee, Hwanhee
- Subjects
Computer Science - Computation and Language - Abstract
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., Comment: 6 figures, 13 tables
- Published
- 2024