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

• URL: http://arxiv.org/abs/2410.13339v1
• Date: Thu, 17 Oct 2024 08:48:54 GMT
• Title: Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval
• Authors: Ingeol Baek, Hwan Chang, Byeongjeong Kim, Jimin Lee, Hwanhee Lee,
• Abstract summary: 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. Probing-RAG effectively captures the model's internal cognition, enabling reliable decision-making about retrieving external documents.
• Score: 3.9639424852746274
• License:
• 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.

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