Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909513528901632 |
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| author | Baek, Ingeol Chang, Hwan Kim, Byeongjeong Lee, Jimin Lee, Hwanhee |
| author_facet | Baek, Ingeol Chang, Hwan Kim, Byeongjeong Lee, Jimin Lee, Hwanhee |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13339 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval Baek, Ingeol Chang, Hwan Kim, Byeongjeong Lee, Jimin Lee, Hwanhee Computation and Language 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. |
| title | Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.13339 |