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Generative information retrieval, encompassing two major tasks of Generative Document Retrieval (GDR) and Grounded Answer Generation (GAR), has gained significant attention in the area of information retrieval and natural language processing.
Language models are few-shot learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 1901
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Lewis, M.; Liu, Y.; Goyal, N.; Ghazvininejad, M.; Mohamed, A.; Levy, O.; Stoyanov, V.; and Zettlemoyer, L. 2019 · 1910
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Huggingface’s transformers: State-of-the-art natural language processing
Wolf, T.; Debut, L.; Sanh, V.; Chaumond, J.; Delangue, C.; Moi, A.; Cistac, P.; Rault, T.; Louf, R.; Funtowicz, M.; et al. 2019 · 1910
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Training question answering models from synthetic data
Puri, R.; Spring, R.; Patwary, M.; Shoeybi, M.; and Catanzaro, B. 2020 · 2002
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Dense passage retrieval for open-domain question answering
Karpukhin, V.; Oğuz, B.; Min, S.; Lewis, P.; Wu, L.; Edunov, S.; Chen, D.; and Yih, W.-t. 2020 · 2004
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Leveraging passage retrieval with generative models for open domain question answering
Izacard, G.; and Grave, E. 2020 · 2007
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Approximate nearest neighbor negative contrastive learning for dense text retrieval
Xiong, L.; Xiong, C.; Li, Y.; Tang, K.-F.; Liu, J.; Bennett, P.; Ahmed, J.; and Overwijk, A. 2020 · 2007
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Generation-augmented retrieval for open-domain question answering
Mao, Y.; He, P.; Liu, X.; Shen, Y.; Gao, J.; Han, J.; and Chen, W. 2020 · 2009
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The probabilistic relevance framework: BM25 and beyond
Robertson, S.; Zaragoza, H.; et al. 2009 · 2009
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Ms marco: A human-generated machine reading comprehension dataset
Nguyen, T.; Rosenberg, M.; Song, X.; Gao, J.; Tiwary, S.; Majumder, R.; and Deng, L. 2016 · 2016
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
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Natural questions: a benchmark for question answering research
Kwiatkowski, T.; Palomaki, J.; Redfield, O.; Collins, M.; Parikh, A.; Alberti, C.; Epstein, D.; Polosukhin, I.; Devlin, J.; Lee, K.; et al. 2019 · 2019
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From doc2query to docTTTTTquery
Nogueira, R.; Lin, J.; and Epistemic, A. 2019 · 2019
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Retrieval augmented language model pre-training
Guu, K.; Lee, K.; Tung, Z.; Pasupat, P.; and Chang, M. 2020 · 2020
Cited alongside, same era.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, P.; Perez, E.; Piktus, A.; Petroni, F.; Karpukhin, V.; Goyal, N.; Küttler, H.; Lewis, M.; Yih, W.-t.; Rocktäschel, T.; et al. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
Cited alongside, same era.
Rethinking search: making domain experts out of dilettantes
Metzler, D.; Tay, Y.; Bahri, D.; and Najork, M. 2021 · 2021
Cited alongside, same era.
End-to-end training of multi-document reader and retriever for open-domain question answering
Singh, D.; Reddy, S.; Hamilton, W.; Dyer, C.; and Yogatama, D. 2021 · 2021
Cited alongside, same era.
Generate rather than retrieve: Large language models are strong context generators
Yu, W.; Iter, D.; Wang, S.; Xu, Y.; Ju, M.; Sanyal, S.; Zhu, C.; Zeng, M.; and Jiang, M. 2022 · 2022
Later among the works it cites.
Zhuang, S.; Ren, H.; Shou, L.; Pei, J.; Gong, M.; Zuccon, G.; and Jiang, D. 2022 · 2022
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Chiang, W.-L.; Li, Z.; Lin, Z.; Sheng, Y.; Wu, Z.; Zhang, H.; Zheng, L.; Zhuang, S.; Zhuang, Y.; Gonzalez, J. E.; et al. 2023 · 2023
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Multiview Identifiers Enhanced Generative Retrieval
Li, Y.; Yang, N.; Wang, L.; Wei, F.; and Li, W. 2023 · 2023
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RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
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Bevilacqua, M.; Ottaviano, G.; Lewis, P.; Yih, W.; Riedel, S.; and Petroni, F. 2022 · 2022
Cited alongside, same era.
Improving language models by retrieving from trillions of tokens
Borgeaud, S.; Mensch, A.; Hoffmann, J.; Cai, T.; Rutherford, E.; Millican, K.; Van Den Driessche, G. B.; Lespiau, J.-B.; Damoc, B.; Clark, A.; et al. 2022 · 2022
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Chowdhery, A.; Narang, S.; Devlin, J.; Bosma, M.; Mishra, G.; Roberts, A.; Barham, P.; Chung, H. W.; Sutton, C.; Gehrmann, S.; et al. 2022 · 2022
Cited alongside, same era.
Few-shot learning with retrieval augmented language models
Izacard, G.; Lewis, P.; Lomeli, M.; Hosseini, L.; Petroni, F.; Schick, T.; Dwivedi-Yu, J.; Joulin, A.; Riedel, S.; and Grave, E. 2022 · 2022
Cited alongside, same era.
DSI++: Updating Transformer Memory with New Documents
Mehta, S. V.; Gupta, J.; Tay, Y.; Dehghani, M.; Tran, V. Q.; Rao, J.; Najork, M.; Strubell, E.; and Metzler, D. 2022 · 2022
Cited alongside, same era.
Recitation-augmented language models
Sun, Z.; Wang, X.; Tay, Y.; Yang, Y.; and Zhou, D. 2022 · 2022
Cited alongside, same era.
Transformer Memory as a Differentiable Search Index
Tay, Y.; Tran, V. Q.; Dehghani, M.; Ni, J.; Bahri, D.; Mehta, H.; Qin, Z.; Hui, K.; Zhao, Z.; Gupta, J. P.; Schuster, T.; Cohen, W. W.; and Metzler, D. 2022 · 2022
Cited alongside, same era.
Liu, J.; Jin, J.; Wang, Z.; Cheng, J.; Dou, Z.; and Wen, J.-R. 2023 · 2023
Closest in time.
In-context retrieval-augmented language models
Ram, O.; Levine, Y.; Dalmedigos, I.; Muhlgay, D.; Shashua, A.; Leyton-Brown, K.; and Shoham, Y. 2023 · 2023
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TOME: A Two-stage Approach for Model-based Retrieval
Ren, R.; Zhao, W. X.; Liu, J.; Wu, H.; Wen, J.; and Wang, H. 2023 · 2023
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Replug: Retrieval-augmented black-box language models
Shi, W.; Min, S.; Yasunaga, M.; Seo, M.; James, R.; Lewis, M.; Zettlemoyer, L.; and Yih, W.-t. 2023 · 2023
Closest in time.
Learning to Tokenize for Generative Retrieval
Sun, W.; Yan, L.; Chen, Z.; Wang, S.; Zhu, H.; Ren, P.; Chen, Z.; Yin, D.; de Rijke, M.; and Ren, Z. 2023 · 2023
Closest in time.
Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies
Tang, Y.; Zhang, R.; Guo, J.; Chen, J.; Zhu, Z.; Wang, S.; Yin, D.; and Cheng, X. 2023 · 2023
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NOVO: Learnable and Interpretable Document Identifiers for Model-Based IR
Wang, Z.; Zhou, Y.; Tu, Y.; and Dou, Z. 2023 · 2023
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Term-Sets Can Be Strong Document Identifiers For Auto-Regressive Search Engines
Zhang, P.; Liu, Z.; Zhou, Y.; Dou, Z.; and Cao, Z. 2023 · 2023
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Enhancing Generative Retrieval with Reinforcement Learning from Relevance Feedback
Zhou, Y.; Dou, Z.; and Wen, J. 2023 · 2023
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