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This paper introduces Stochastic RAG--a novel approach for end-to-end optimization of retrieval-augmented generation (RAG) models that relaxes the simplifying assumptions of marginalization and document independence, made in most prior work.
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Autoregressive Search Engines: Generating Substrings as Document Identifiers
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Dense passage retrieval for open-domain question answering
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Retrieval-augmented generation for knowledge-intensive nlp tasks
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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Hindsight: Posterior-guided training of retrievers for improved open-ended generation
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Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator. In International Conference on Learning Representations (ICLR ’21)
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KILT: a Benchmark for Knowledge Intensive Language Tasks. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021
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Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Rajaram Naik, Pengshan Cai, and Alfio Gliozzo. 2022b · 2022
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Multi-Task Retrieval-Augmented Text Generation with Relevance Sampling. In ICML 2022 Workshop on Knowledge Retrieval and Language Models
Sebastian Hofstätter, Jiecao Chen, Karthik Raman, and Hamed Zamani. 2022 · 2022
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A Survey on Retrieval-Augmented Text Generation
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WebGPT: Browser-assisted question-answering with human feedback
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Scaling Up Models and Data with t5x
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Stochastic Retrieval-Conditioned Reranking. In Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval (Madrid, Spain) (ICTIR ’22) . Association for Computing Machinery, New York, NY, USA, 81–91
Hamed Zamani, Michael Bendersky, Donald Metzler, Honglei Zhuang, and Xuanhui Wang. 2022a · 2022
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GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (Birmingham, United Kingdom) (CIKM ’23) . Association for Computing Machinery, New York, NY, USA, 36–46
Jiaqi Bai, Hongcheng Guo, Jiaheng Liu, Jian Yang, Xinnian Liang, Zhao Yan, and Zhoujun Li. 2023 · 2023
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FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (Taipei, Taiwan) (SIGIR ’23) . Association for Computing Machinery, New York, NY, USA, 1437–1447
Sebastian Hofstätter, Jiecao Chen, Karthik Raman, and Hamed Zamani. 2023 · 2023
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REPLUG: Retrieval-Augmented Black-Box Language Models
Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. 2023 · 2023
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On the Planning Abilities of Large Language Models - A Critical Investigation
Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, and Subbarao Kambhampati. 2023 · 2023
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FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation. In arXiv
Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, and Thang Luong. 2023 · 2023
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Evaluating Retrieval Quality in Retrieval-Augmented Generation. In Proceedings of the 47th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Washington, DC, USA) (SIGIR ’24)
Alireza Salemi and Hamed Zamani. 2024 · 2024
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Re3val: Reinforced and Reranked Generative Retrieval
EuiYul Song, Sangryul Kim, Haeju Lee, Joonkee Kim, and James Thorne. 2024 · 2024
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