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Retrieval-augmented generation (RAG) is a popular technique for using large language models (LLMs) to build customer-support, question-answering solutions.
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2020 · 1905
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AmbigQA: Answering Ambiguous Open-domain Questions
Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2020 · 2004
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DART: Open-Domain Structured Data Record to Text Generation
Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, and Nazneen Fatema Rajani. 2021 · 2007
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The impact of wikipedia on scientific research
Richard Khoury. 2009 · 2009
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Generation-Augmented Retrieval for Open-domain Question Answering
Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, and Weizhu Chen. 2021 · 2009
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KILT: a Benchmark for Knowledge Intensive Language Tasks
Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktäschel, and Sebastian Riedel. 2021 · 2009
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WikiAsp: A Dataset for Multi-domain Aspect-based Summarization
Hiroaki Hayashi, Prashant Budania, Peng Wang, Chris Ackerson, Raj Neervannan, and Graham Neubig. 2020 · 2011
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Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. 2020 · 2011
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Neural Text Generation from Structured Data with Application to the Biography Domain
Remi Lebret, David Grangier, and Michael Auli. 2016 · 2016
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SQuAD: 100,000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer. 2017 · 2017
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Science Is Shaped by Wikipedia: Evidence From a Randomized Control Trial
Neil Thompson and Douglas Hanley. 2018 · 2018
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FEVER: a large-scale dataset for Fact Extraction and VERification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
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HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
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Wizard of Wikipedia: Knowledge-Powered Conversational agents
Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2019 · 2019
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Natural Questions: a Benchmark for Question Answering Research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
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Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021 · 2021
Cited alongside, same era.
End-to-End Table Question Answering via Retrieval-Augmented Generation
Feifei Pan, Mustafa Canim, Michael Glass, Alfio Gliozzo, and James Hendler. 2022 · 2022
Cited alongside, same era.
Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Tharindu Kaluarachchi, Rajib Rana, and Suranga Nanayakkara. 2022 · 2022
Cited alongside, same era.
MuSiQue: Multihop Questions via Single-hop Question Composition
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022 · 2022
Cited alongside, same era.
HiQA: A Hierarchical Contextual Augmentation RAG for Massive Documents QA
Xinyue Chen, Pengyu Gao, Jiangjiang Song, and Xiaoyang Tan. 2024 · 2024
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The Power of Noise: Redefining Retrieval for RAG Systems
Florin Cuconasu, Giovanni Trappolini, Federico Siciliano, Simone Filice, Cesare Campagnano, Yoelle Maarek, Nicola Tonellotto, and Fabrizio Silvestri. 2024 · 2024
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InspectorRAGet: An Introspection Platform for RAG Evaluation
Kshitij Fadnis, Siva Sankalp Patel, Odellia Boni, Yannis Katsis, Sara Rosenthal, Benjamin Sznajder, and Marina Danilevsky. 2024 · 2024
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T-RAG: Lessons from the LLM Trenches
Masoomali Fatehkia, Ji Kim Lucas, and Sanjay Chawla. 2024 · 2024
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Lukas Blecher, Guillem Cucurull, Thomas Scialom, and Robert Stojnic. 2023 · 2023
Cited alongside, same era.
I-Chun Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, and Pengfei Liu. 2023 · 2023
Cited alongside, same era.
RAGAS: Automated Evaluation of Retrieval Augmented Generation
Shahul Es, Jithin James, Luis Espinosa-Anke, and Steven Schockaert. 2023 · 2023
Cited alongside, same era.
Active Retrieval Augmented Generation
Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023 · 2023
Cited alongside, same era.
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
Cited alongside, same era.
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023 · 2023
Cited alongside, same era.
Anupam Purwar and Rahul Sundar. 2023 · 2023
Cited alongside, same era.
ASQA: Factoid Questions Meet Long-Form Answers
Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, and Ming-Wei Chang. 2023 · 2023
Cited alongside, same era.
Philip Feldman. James R. Foulds and Shimei Pan. 2024 · 2024
Closest in time.
Retrieval-Augmented Generation for Large Language Models: A Survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. 2024 · 2024
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Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition
Demiao Lin. 2024 · 2024
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Rag-Fusion: A New Take on Retrieval Augmented Generation
Zackary Rackauckas. 2024 · 2024
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ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems
Jon Saad-Falcon, Omar Khattab, Christopher Potts, and Matei Zaharia. 2024 · 2024
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Evaluating Retrieval Quality in Retrieval-Augmented Generation
Alireza Salemi and Hamed Zamani. 2024 · 2024
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Kunal Sawarkar, Abhilasha Mangal, and Shivam Raj Solanki. 2024 · 2024
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NoMIRACL: Knowing When You Don’t Know for Robust Multilingual Retrieval-Augmented Generation
Nandan Thakur, Luiz Bonifacio, Xinyu Zhang, Odunayo Ogundepo, Ehsan Kamalloo, David Alfonso-Hermelo, Xiaoguang Li, Qun Liu, Boxing Chen, Mehdi Rezagholizadeh, and Jimmy Lin. 2024 · 2024
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How faithful are RAG models? Quantifying the tug-of-war between RAG and LLMs’ internal prior
Kevin Wu, Eric Wu, and James Zou. 2024 · 2024
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Corrective Retrieval Augmented Generation
Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling. 2024 · 2024
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Evaluation of Retrieval-Augmented Generation: A Survey
Hao Yu, Aoran Gan, Kai Zhang, Shiwei Tong, Qi Liu, and Zhaofeng Liu. 2024 · 2024
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RAFT: Adapting Language Model to Domain Specific RAG
Tianjun Zhang, Shishir G. Patil, Naman Jain, Sheng Shen, Matei Zaharia, Ion Stoica, and Joseph E. Gonzalez. 2024 · 2024
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