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Dense retrieval systems have been widely used in various NLP applications.
The probabilistic relevance framework: BM25 and beyond
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Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang · 2020
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Dense passage retrieval for open-domain question answering
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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Triggerless backdoor attack for nlp tasks with clean labels
Leilei Gan, Jiwei Li, Tianwei Zhang, Xiaoya Li, Yuxian Meng, Fei Wu, Yi Yang, Shangwei Guo, and Chun Fan · 2022
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Backdoor learning: A survey
Yiming Li, Yong Jiang, Zhifeng Li, and Shu-Tao Xia · 2022
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Replug: Retrieval-augmented black-box language models
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Large language models are better adversaries: Exploring generative clean-label backdoor attacks against text classifiers
Wencong You, Zayd Hammoudeh, and Daniel Lowd · 2023
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Enhancing the ranking context of dense retrieval through reciprocal nearest neighbors
George Zerveas, Navid Rekabsaz, and Carsten Eickhoff · 2023
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Approximate nearest neighbor negative contrastive learning for dense text retrieval
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On the robustness of language encoders against grammatical errors
Fan Yin, Quanyu Long, Tao Meng, and Kai-Wei Chang · 2020
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Badnl: Backdoor attacks against nlp models with semantic-preserving improvements
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Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al · 2022
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Unsupervised dense information retrieval with contrastive learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave
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Few-shot learning with retrieval augmented language models
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave
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Prompt as triggers for backdoor attack: Examining the vulnerability in language models
Shuai Zhao, Jinming Wen, Anh Luu, Junbo Zhao, and Jie Fu · 2023
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Poisoning retrieval corpora by injecting adversarial passages
Zexuan Zhong, Ziqing Huang, Alexander Wettig, and Danqi Chen · 2023
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Phantom: General trigger attacks on retrieval augmented language generation
Harsh Chaudhari, Giorgio Severi, John Abascal, Matthew Jagielski, Christopher A Choquette-Choo, Milad Nasr, Cristina Nita-Rotaru, and Alina Oprea · 2024
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Glue pizza and eat rocks - exploiting vulnerabilities in retrieval-augmented generative models
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