@inproceedings{zhang-etal-2026-d2,
title = "D$^2$-{RAG}: Dual-Decision Retrieval-Augmented Generation via Multi-Dimensional Uncertainty and Utility-Aware Decoding",
author = "Zhang, Jinshuo and
Zhou, Xiaoding and
Zhang, Weiyu and
Chen, Guoqiang and
Lian, Ying and
Meng, Xiaoyang and
Chen, Yonghe and
Guan, Hongjiao and
Si, Jiasheng and
Lu, Wenpeng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.754/",
doi = "10.18653/v1/2026.findings-acl.754",
pages = "15355--15382",
ISBN = "979-8-89176-395-1",
abstract = "Retrieval-Augmented Generation (RAG) mitigates hallucinations in large language models by incorporating external knowledge. However, retrieval does not always return relevant documents and may return noisy ones. Indiscriminately retrieving and utilizing this external knowledge can interfere with the model{'}s originally correct reasoning. In this work, we propose Dual-Decision Retrieval-Augmented Generation (D$^2$-RAG), which integrates multi-dimensional uncertainty estimation to decide whether to retrieve and employs adaptive contrastive decoding to handle retrieved contexts of varying quality. Specifically, we first integrate uncertainty estimation scores that assess model uncertainty from multiple perspectives, construct them into a comprehensive feature vector, and train a lightweight retrieval decision model to accurately identify the model{'}s knowledge boundaries and determine whether to retrieve. Subsequently, we dynamically adjust the contrastive decoding strategy based on the utility of retrieved contexts to enhance the utilization of relevant contexts while suppressing interference from noisy contexts. Extensive experiments on four medical question-answering datasets demonstrate that D$^2$-RAG significantly outperforms baselines, enabling retrieval-augmented Llama3.1-8B to surpass non-retrieval-augmented Llama3.1-70B on the MedMCQA dataset. The source code is available on https://github.com/zakelawen/d{--}rag."
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<abstract>Retrieval-Augmented Generation (RAG) mitigates hallucinations in large language models by incorporating external knowledge. However, retrieval does not always return relevant documents and may return noisy ones. Indiscriminately retrieving and utilizing this external knowledge can interfere with the model’s originally correct reasoning. In this work, we propose Dual-Decision Retrieval-Augmented Generation (D²-RAG), which integrates multi-dimensional uncertainty estimation to decide whether to retrieve and employs adaptive contrastive decoding to handle retrieved contexts of varying quality. Specifically, we first integrate uncertainty estimation scores that assess model uncertainty from multiple perspectives, construct them into a comprehensive feature vector, and train a lightweight retrieval decision model to accurately identify the model’s knowledge boundaries and determine whether to retrieve. Subsequently, we dynamically adjust the contrastive decoding strategy based on the utility of retrieved contexts to enhance the utilization of relevant contexts while suppressing interference from noisy contexts. Extensive experiments on four medical question-answering datasets demonstrate that D²-RAG significantly outperforms baselines, enabling retrieval-augmented Llama3.1-8B to surpass non-retrieval-augmented Llama3.1-70B on the MedMCQA dataset. The source code is available on https://github.com/zakelawen/d–rag.</abstract>
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%0 Conference Proceedings
%T D²-RAG: Dual-Decision Retrieval-Augmented Generation via Multi-Dimensional Uncertainty and Utility-Aware Decoding
%A Zhang, Jinshuo
%A Zhou, Xiaoding
%A Zhang, Weiyu
%A Chen, Guoqiang
%A Lian, Ying
%A Meng, Xiaoyang
%A Chen, Yonghe
%A Guan, Hongjiao
%A Si, Jiasheng
%A Lu, Wenpeng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhang-etal-2026-d2
%X Retrieval-Augmented Generation (RAG) mitigates hallucinations in large language models by incorporating external knowledge. However, retrieval does not always return relevant documents and may return noisy ones. Indiscriminately retrieving and utilizing this external knowledge can interfere with the model’s originally correct reasoning. In this work, we propose Dual-Decision Retrieval-Augmented Generation (D²-RAG), which integrates multi-dimensional uncertainty estimation to decide whether to retrieve and employs adaptive contrastive decoding to handle retrieved contexts of varying quality. Specifically, we first integrate uncertainty estimation scores that assess model uncertainty from multiple perspectives, construct them into a comprehensive feature vector, and train a lightweight retrieval decision model to accurately identify the model’s knowledge boundaries and determine whether to retrieve. Subsequently, we dynamically adjust the contrastive decoding strategy based on the utility of retrieved contexts to enhance the utilization of relevant contexts while suppressing interference from noisy contexts. Extensive experiments on four medical question-answering datasets demonstrate that D²-RAG significantly outperforms baselines, enabling retrieval-augmented Llama3.1-8B to surpass non-retrieval-augmented Llama3.1-70B on the MedMCQA dataset. The source code is available on https://github.com/zakelawen/d–rag.
%R 10.18653/v1/2026.findings-acl.754
%U https://aclanthology.org/2026.findings-acl.754/
%U https://doi.org/10.18653/v1/2026.findings-acl.754
%P 15355-15382
Markdown (Informal)
[D2-RAG: Dual-Decision Retrieval-Augmented Generation via Multi-Dimensional Uncertainty and Utility-Aware Decoding](https://aclanthology.org/2026.findings-acl.754/) (Zhang et al., Findings 2026)
ACL
- Jinshuo Zhang, Xiaoding Zhou, Weiyu Zhang, Guoqiang Chen, Ying Lian, Xiaoyang Meng, Yonghe Chen, Hongjiao Guan, Jiasheng Si, and Wenpeng Lu. 2026. D2-RAG: Dual-Decision Retrieval-Augmented Generation via Multi-Dimensional Uncertainty and Utility-Aware Decoding. In Findings of the Association for Computational Linguistics: ACL 2026, pages 15355–15382, San Diego, California, United States. Association for Computational Linguistics.