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Retrieval Augmented Generation (RAG) expands the capabilities of modern large language models (LLMs), by anchoring, adapting, and personalizing their responses to the most relevant knowledge sources.
Enron email dataset
J. Shetty and J. Adibi · 2004
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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HotFlip: White-Box Adversarial Examples for Text Classification
J. Ebrahimi, A. Rao, D. Lowd, and D. Dou · 2018
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Textbugger: Generating adversarial text against real-world applications
J. Li, S. Ji, T. Du, B. Li, and T. Wang · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
A. Shafahi, W. R. Huang, M. Najibi, O. Suciu, C. Studer, T. Dumitras, and T. Goldstein · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
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BadNets: Evaluating Backdooring Attacks on Deep Neural Networks
T. Gu, K. Liu, B. Dolan-Gavitt, and S. Garg · 2019
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Dense passage retrieval for open-domain question answering
V. Karpukhin, B. Oguz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, and W.-t. Yih · 2020
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Biobert: a pre-trained biomedical language representation model for biomedical text mining
J. Lee, W. Yoon, S. Kim, D. Kim, S. Kim, C. H. So, and J. Kang · 2020
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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, S. Riedel, and D. Kiela · 2020
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
T. Shin, Y. Razeghi, R. L. Logan IV, E. Wallace, and S. Singh · 2020
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Extracting Training Data from Large Language Models
N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song, U. Erlingsson, A. Oprea, and C. Raffel · 2021
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Witches’ brew: Industrial scale data poisoning via gradient matching
J. Geiping, L. H. Fowl, W. R. Huang, W. Czaja, G. Taylor, M. Moeller, and T. Goldstein · 2021
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Retrieval augmentation reduces hallucination in conversation
K. Shuster, S. Poff, M. Chen, D. Kiela, and J. Weston · 2021
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Unsupervised dense information retrieval with contrastive learning
I. Gautier, C. Mathilde, H. Lucas, R. Sebastian, B. Piotr, J. Armand, and G. Edouard · 2022
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Unsupervised dense information retrieval with contrastive learning
G. Izacard, M. Caron, L. Hosseini, S. Riedel, P. Bojanowski, A. Joulin, and E. Grave · 2022
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez, I. Stoica, and E. P. Xing · 2023
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K. Greshake, S. Abdelnabi, S. Mishra, C. Endres, T. Holz, and M. Fritz · 2023
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Catastrophic jailbreak of open-source llms via exploiting generation
Y. Huang, S. Gupta, M. Xia, K. Li, and D. Chen · 2023
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Baseline defenses for adversarial attacks against aligned language models
N. Jain, A. Schwarzschild, Y. Wen, G. Somepalli, J. Kirchenbauer, P. Chiang, M. Goldblum, A. Saha, J. Geiping, and T. Goldstein · 2023
Cited alongside, same era.
Mistral 7b, 2023
A. Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D. S. Chaplot, D. de las Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier, L. R. Lavaud, M.-A. Lachaux, P. Stock, T. L. Scao, T. Lavril, T. Wang, T. Lacroix, and W. E. Sayed · 2023
Cited alongside, same era.
Membership inference attacks against language models via neighbourhood comparison
J. Mattern, F. Mireshghallah, Z. Jin, B. Schölkopf, M. Sachan, and T. Berg-Kirkpatrick · 2023
Cited alongside, same era.
Scalable extraction of training data from (production) language models
M. Nasr, N. Carlini, J. Hayase, M. Jagielski, A. F. Cooper, D. Ippolito, C. A. Choquette-Choo, E. Wallace, F. Tramèr, and K. Lee · 2023
Cited alongside, same era.
Cohere Toolkit
Cohere · 2024
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Flocks of stochastic parrots: Differentially private prompt learning for large language models
H. Duan, A. Dziedzic, N. Papernot, and F. Boenisch · 2024
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Do membership inference attacks work on large language models?
M. Duan, A. Suri, N. Mireshghallah, S. Min, W. Shi, L. Zettlemoyer, Y. Tsvetkov, Y. Choi, D. Evans, and H. Hajishirzi · 2024
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Build safe and responsible generative AI applications with guardrails
H. Gal, E. Sela, G. Nachum, , and M. Mayer · 2024
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Google VertexAI RAG chat
Google · 2024
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Data poisoning for in-context learning, 2024
P. He, H. Xu, Y. Xing, H. Liu, M. Yamada, and J. Tang · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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B. Pan, N. Stakhanova, and S. Ray · 2023
Cited alongside, same era.
NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails, 2023
T. Rebedea, R. Dinu, M. Sreedhar, C. Parisien, and J. Cohen · 2023
Cited alongside, same era.
On the exploitability of instruction tuning
M. Shu, J. Wang, C. Zhu, J. Geiping, C. Xiao, and T. Goldstein · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
G. Team, R. Anil, S. Borgeaud, Y. Wu, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, et al · 2023
Cited alongside, same era.
LLaMA: Open and efficient foundation language models, 2023
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, and G. Lample · 2023
Cited alongside, same era.
Poisoning language models during instruction tuning, 2023
A. Wan, E. Wallace, S. Shen, and D. Klein · 2023
Cited alongside, same era.
Privacy-preserving in-context learning for large language models
T. Wu, A. Panda, J. T. Wang, and P. Mittal · 2023
Cited alongside, same era.
Low-resource languages jailbreak gpt-4
Z.-X. Yong, C. Menghini, and S. H. Bach · 2023
Cited alongside, same era.
Sleeper agents: Training deceptive llms that persist through safety training, 2024
E. Hubinger, C. Denison, J. Mu, M. Lambert, M. Tong, M. MacDiarmid, T. Lanham, D. M. Ziegler, T. Maxwell, N. Cheng, A. Jermyn, A. Askell, A. Radhakrishnan, C. Anil, D. Duvenaud, D. Ganguli, F. Barez, J. Clark, K. Ndousse, K. Sachan, M. Sellitto, M. Sharma, N. DasSarma, R. Grosse, S. Kravec, Y. Bai, Z. Witten, M. Favaro, J. Brauner, H. Karnofsky, P. Christiano, S. R. Bowman, L. Graham, J. Kaplan, S. Mindermann, R. Greenblatt, B. Shlegeris, N. Schiefer, and E. Perez · 2024
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Meta Llama 3
Meta · 2024
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Microsoft Copilot
Microsoft · 2024
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Microsoft RAG in Azure Search
Microsoft · 2024
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NVIDIA Chat with RTX
Nvidia · 2024
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Neural exec: Learning (and learning from) execution triggers for prompt injection attacks
D. Pasquini, M. Strohmeier, and C. Troncoso · 2024
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Universal jailbreak backdoors from poisoned human feedback
J. Rando and F. Tramèr · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
M. Reid, N. Savinov, D. Teplyashin, D. Lepikhin, T. Lillicrap, J.-b. Alayrac, R. Soricut, A. Lazaridou, O. Firat, J. Schrittwieser, et al · 2024
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Machine against the rag: Jamming retrieval-augmented generation with blocker documents, 2024
A. Shafran, R. Schuster, and V. Shmatikov · 2024
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Gemma: Open models based on gemini research and technology
G. Team, T. Mesnard, C. Hardin, R. Dadashi, S. Bhupatiraju, S. Pathak, L. Sifre, M. Rivière, M. S. Kale, J. Love, et al · 2024
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Certifiably robust rag against retrieval corruption, 2024
C. Xiang, T. Wu, Z. Zhong, D. Wagner, D. Chen, and P. Mittal · 2024
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Badrag: Identifying vulnerabilities in retrieval augmented generation of large language models
J. Xue, M. Zheng, Y. Hu, F. Liu, X. Chen, and Q. Lou · 2024
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PoisonedRAG: Knowledge poisoning attacks to retrieval-augmented generation of large language models
W. Zou, R. Geng, B. Wang, and J. Jia · 2024
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