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    <abstract>Retrieval-Augmented Generation (RAG) systems traditionally treat retrieval and generation as separate processes, requiring explicit textual queries to connect them. This separation can limit the ability of models to generalize across diverse tasks. In this work, we propose a query-free RAG system, named ImpRAG, which integrates retrieval and generation into a unified model. ImpRAG allows models to implicitly express their information needs, eliminating the need for human-specified queries. By dividing pretrained decoder-only language models into specialized layer groups, ImpRAG optimizes retrieval and generation tasks simultaneously. Our approach employs a two-stage inference process, using the same model parameters and forward pass for both retrieval and generation, thereby minimizing the disparity between retrievers and language models. Experiments on 8 knowledge-intensive tasks demonstrate that ImpRAG achieves 3.6-11.5 improvements in exact match scores on unseen tasks with diverse formats, highlighting its effectiveness in enabling models to articulate their own information needs and generalize across tasks. Our analysis underscores the importance of balancing retrieval and generation parameters and leveraging generation perplexities as retrieval training objectives for enhanced performance.</abstract>
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%0 Conference Proceedings
%T ImpRAG: Retrieval-Augmented Generation with Implicit Queries
%A Zhang, Wenzheng
%A Lin, Xi Victoria
%A Stratos, Karl
%A Yih, Wen-tau
%A Chen, Mingda
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F zhang-etal-2025-imprag
%X Retrieval-Augmented Generation (RAG) systems traditionally treat retrieval and generation as separate processes, requiring explicit textual queries to connect them. This separation can limit the ability of models to generalize across diverse tasks. In this work, we propose a query-free RAG system, named ImpRAG, which integrates retrieval and generation into a unified model. ImpRAG allows models to implicitly express their information needs, eliminating the need for human-specified queries. By dividing pretrained decoder-only language models into specialized layer groups, ImpRAG optimizes retrieval and generation tasks simultaneously. Our approach employs a two-stage inference process, using the same model parameters and forward pass for both retrieval and generation, thereby minimizing the disparity between retrievers and language models. Experiments on 8 knowledge-intensive tasks demonstrate that ImpRAG achieves 3.6-11.5 improvements in exact match scores on unseen tasks with diverse formats, highlighting its effectiveness in enabling models to articulate their own information needs and generalize across tasks. Our analysis underscores the importance of balancing retrieval and generation parameters and leveraging generation perplexities as retrieval training objectives for enhanced performance.
%R 10.18653/v1/2025.findings-emnlp.472
%U https://aclanthology.org/2025.findings-emnlp.472/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.472
%P 8888-8900

Markdown (Informal)

[ImpRAG: Retrieval-Augmented Generation with Implicit Queries](https://aclanthology.org/2025.findings-emnlp.472/) (Zhang et al., Findings 2025)

• ImpRAG: Retrieval-Augmented Generation with Implicit Queries (Zhang et al., Findings 2025)

ACL

• Wenzheng Zhang, Xi Victoria Lin, Karl Stratos, Wen-tau Yih, and Mingda Chen. 2025. ImpRAG: Retrieval-Augmented Generation with Implicit Queries. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 8888–8900, Suzhou, China. Association for Computational Linguistics.