@inproceedings{dai-etal-2026-evinoterag,
title = "{E}vi{N}ote{RAG}: Enhancing {RAG} Models via Quality-Augmented Evidence Notes",
author = "Dai, Yuqin and
Wang, Guoqing and
Wang, Yuan and
Yao, Zhouheng and
Dou, Kairan and
Zhou, Kaichen and
Zhang, Zhanwei and
Yang, Shuo and
Tang, Fei and
Yin, Jun and
Zeng, Pengyu and
YI, Can and
Meng, Changhua and
Zhou, Yuchen and
Shen, Yongliang and
Lu, Shuai",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2108/",
pages = "47970--47990",
ISBN = "979-8-89176-459-0",
abstract = "Retrieval-Augmented Generation (RAG) has advanced open-domain question answering by incorporating external information into model reasoning. However, raw retrieved content often suffers from low signal-to-noise ratio, where answer-supportive information is diluted by irrelevant material, and error accumulation, where incomplete or misleading content degrades multi-hop reasoning. To address these challenges, we propose EviNoteRAG, a framework that enhances RAG through quality-augmented evidence notes. The model is trained to generate Supportive-Evidence Notes (SENs), concise abstractions that preserve only answer-critical information and explicitly highlight key and uncertain content to improve reasoning accuracy. To further ensure note quality, we introduce an entailment-based Evidence Quality Reward (EQR), which incentivizes SENs to be logically sufficient for deriving the correct answer. Experiments on both in-domain and out-of-domain benchmarks demonstrate that EviNoteRAG achieves state-of-the-art performance, with relative F1 gains of 20{\%} on HotpotQA (+0.093), 40{\%} on Bamboogle (+0.151), and 91{\%} on 2Wiki (+0.256), while also improving training stability and efficiency."
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<abstract>Retrieval-Augmented Generation (RAG) has advanced open-domain question answering by incorporating external information into model reasoning. However, raw retrieved content often suffers from low signal-to-noise ratio, where answer-supportive information is diluted by irrelevant material, and error accumulation, where incomplete or misleading content degrades multi-hop reasoning. To address these challenges, we propose EviNoteRAG, a framework that enhances RAG through quality-augmented evidence notes. The model is trained to generate Supportive-Evidence Notes (SENs), concise abstractions that preserve only answer-critical information and explicitly highlight key and uncertain content to improve reasoning accuracy. To further ensure note quality, we introduce an entailment-based Evidence Quality Reward (EQR), which incentivizes SENs to be logically sufficient for deriving the correct answer. Experiments on both in-domain and out-of-domain benchmarks demonstrate that EviNoteRAG achieves state-of-the-art performance, with relative F1 gains of 20% on HotpotQA (+0.093), 40% on Bamboogle (+0.151), and 91% on 2Wiki (+0.256), while also improving training stability and efficiency.</abstract>
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%0 Conference Proceedings
%T EviNoteRAG: Enhancing RAG Models via Quality-Augmented Evidence Notes
%A Dai, Yuqin
%A Wang, Guoqing
%A Wang, Yuan
%A Yao, Zhouheng
%A Dou, Kairan
%A Zhou, Kaichen
%A Zhang, Zhanwei
%A Yang, Shuo
%A Tang, Fei
%A Yin, Jun
%A Zeng, Pengyu
%A YI, Can
%A Meng, Changhua
%A Zhou, Yuchen
%A Shen, Yongliang
%A Lu, Shuai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F dai-etal-2026-evinoterag
%X Retrieval-Augmented Generation (RAG) has advanced open-domain question answering by incorporating external information into model reasoning. However, raw retrieved content often suffers from low signal-to-noise ratio, where answer-supportive information is diluted by irrelevant material, and error accumulation, where incomplete or misleading content degrades multi-hop reasoning. To address these challenges, we propose EviNoteRAG, a framework that enhances RAG through quality-augmented evidence notes. The model is trained to generate Supportive-Evidence Notes (SENs), concise abstractions that preserve only answer-critical information and explicitly highlight key and uncertain content to improve reasoning accuracy. To further ensure note quality, we introduce an entailment-based Evidence Quality Reward (EQR), which incentivizes SENs to be logically sufficient for deriving the correct answer. Experiments on both in-domain and out-of-domain benchmarks demonstrate that EviNoteRAG achieves state-of-the-art performance, with relative F1 gains of 20% on HotpotQA (+0.093), 40% on Bamboogle (+0.151), and 91% on 2Wiki (+0.256), while also improving training stability and efficiency.
%U https://aclanthology.org/2026.emnlp-main.2108/
%P 47970-47990
Markdown (Informal)
[EviNoteRAG: Enhancing RAG Models via Quality-Augmented Evidence Notes](https://aclanthology.org/2026.emnlp-main.2108/) (Dai et al., EMNLP 2026)
ACL
- Yuqin Dai, Guoqing Wang, Yuan Wang, Zhouheng Yao, Kairan Dou, Kaichen Zhou, Zhanwei Zhang, Shuo Yang, Fei Tang, Jun Yin, Pengyu Zeng, Can YI, Changhua Meng, Yuchen Zhou, Yongliang Shen, and Shuai Lu. 2026. EviNoteRAG: Enhancing RAG Models via Quality-Augmented Evidence Notes. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 47970–47990, Budapest, Hungary. Association for Computational Linguistics.