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Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model with proprietary and private data, where data privacy is a pivotal concern.
Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2019 · 1911
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Discriminatory analysis. nonparametric discrimination: Consistency properties
Evelyn Fix and Joseph Lawson Hodges. 1989 · 1989
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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 · 2020
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. 2021 · 2021
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Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini. 2021 · 2021
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Retrieval augmented code generation and summarization
Md Rizwan Parvez, Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
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Retrieval augmentation reduces hallucination in conversation
Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021 · 2021
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An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2021 · 2021
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Counterfactual memorization in neural language models
Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski, Florian Tramèr, and Nicholas Carlini. 2021 · 2021
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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 · 2022
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Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang. 2022 · 2022
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Langchain
Harrison Chase. 2022 · 2022
Cited alongside, same era.
Preventing verbatim memorization in language models gives a false sense of privacy
Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A Choquette-Choo, and Nicholas Carlini. 2022 · 2022
Cited alongside, same era.
Deduplicating training data mitigates privacy risks in language models
Nikhil Kandpal, Eric Wallace, and Colin Raffel. 2022 · 2022
Cited alongside, same era.
Llamaindex
Jerry Liu. 2022 · 2022
Cited alongside, same era.
Memorization in nlp fine-tuning methods
Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang, David Evans, and Taylor Berg-Kirkpatrick. 2022 · 2022
Cited alongside, same era.
Prompt injection attacks against gpt-3
Twitter post
Liu. 2023 · 2023
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In-context retrieval-augmented language models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023 · 2023
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Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy
Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen. 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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Improving the domain adaptation of retrieval augmented generation (rag) models for open domain question answering
Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Tharindu Kaluarachchi, Rajib Rana, and Suranga Nanayakkara. 2023 · 2023
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Simon Willison. 2022 · 2022
Cited alongside, same era.
Emergent and predictable memorization in large language models
Stella Biderman, USVSN Sai Prashanth, Lintang Sutawika, Hailey Schoelkopf, Quentin Anthony, Shivanshu Purohit, and Edward Raf. 2023 · 2023
Cited alongside, same era.
Lift yourself up: Retrieval-augmented text generation with self memory
Xin Cheng, Di Luo, Xiuying Chen, Lemao Liu, Dongyan Zhao, and Rui Yan. 2023 · 2023
Cited alongside, same era.
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, and Haofen Wang. 2023 · 2023
Cited alongside, same era.
Privacy implications of retrieval-based language models
Yangsibo Huang, Samyak Gupta, Zexuan Zhong, Kai Li, and Danqi Chen. 2023 · 2023
Cited alongside, same era.
Do language models plagiarize?
Jooyoung Lee, Thai Le, Jinghui Chen, and Dongwon Lee. 2023 · 2023
Cited alongside, same era.
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Clinical text summarization: Adapting large language models can outperform human experts
Dave Van Veen, Cara Van Uden, Louis Blankemeier, Jean-Benoit Delbrouck, Asad Aali, Christian Bluethgen, Anuj Pareek, Malgorzata Polacin, William Collins, Neera Ahuja, et al. 2023 · 2023
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Chatdoctor: A medical chat model fine-tuned on llama model using medical domain knowledge
Li Yunxiang, Li Zihan, Zhang Kai, Dan Ruilong, and Zhang You. 2023 · 2023
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Exploring memorization in fine-tuned language models
Shenglai Zeng, Yaxin Li, Jie Ren, Yiding Liu, Han Xu, Pengfei He, Yue Xing, Shuaiqiang Wang, Jiliang Tang, and Dawei Yin. 2023 · 2023
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Prompts should not be seen as secrets: Systematically measuring prompt extraction attack success
Yiming Zhang and Daphne Ippolito. 2023 · 2023
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Reinforcement learning for optimizing rag for domain chatbots
Mandar Kulkarni, Praveen Tangarajan, Kyung Kim, and Anusua Trivedi. 2024 · 2024
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Augmenting large language models with rules for enhanced domain-specific interactions: The case of medical diagnosis
Dimitrios P Panagoulias, Maria Virvou, and George A Tsihrintzis. 2024 · 2024
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