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Generation with source attribution is important for enhancing the verifiability of retrieval-augmented generation (RAG) systems.
Object detection in 20 years: A survey
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015 · 2015
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R-fcn: Object detection via region-based fully convolutional networks
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You only look once: Unified, real-time object detection
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Object detection with deep learning: A review
Zhong-Qiu Zhao, Peng Zheng, Shou tao Xu, and Xindong Wu. 2019 · 2019
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PubLayNet: Largest Dataset Ever for Document Layout Analysis
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Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
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Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021 · 2021
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Bernd Bohnet, Vinh Q. Tran, Pat Verga, Roee Aharoni, Daniel Andor, Livio Baldini Soares, Massimiliano Ciaramita, Jacob Eisenstein, Kuzman Ganchev, Jonathan Herzig, Kai Hui, Tom Kwiatkowski, Ji Ma, Jianmo Ni, Lierni Sestorain Saralegui, Tal Schuster, William W. Cohen, Michael Collins, Dipanjan Das, Donald Metzler, Slav Petrov, and Kellie Webster. 2023 · 2023
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Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023 · 2023
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Measuring attribution in natural language generation models
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Reliable, adaptable, and attributable language models with retrieval
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M3DocRAG: Multi-modal retrieval is what you need for multi-page multi-document understanding
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Retrieval-augmented generation for large language models: A survey
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Source-aware training enables knowledge attribution in language models
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