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Large Language Models (LLMs) enable a new ecosystem with many downstream applications, called LLM applications, with different natural language processing tasks.
B. Pang and L. Lee, “Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales,” in Proceedings of the ACL , 2005
2005
Earlier work this paper cites.
J. Ebrahimi, A. Rao, D. Lowd, and D. Dou, “HotFlip: White-box adversarial examples for text classification,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , I. Gurevych and Y. Miyao, Eds. Melbourne, Australia: Association for Computational Linguistics, Jul. 2018, pp. 31–36. [Online]. Available: https://aclanthology.org/P18-2006
2006
Earlier work this paper cites.
P. Malo, A. Sinha, P. Korhonen, J. Wallenius, and P. Takala, “Good debt or bad debt: Detecting semantic orientations in economic texts,” Journal of the Association for Information Science and Technology , vol. 65, 2014
2014
Earlier work this paper cites.
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing machine learning models via prediction { \{ APIs } \} ,” in USENIX security symposium , 2016, pp. 601–618
2016
Earlier work this paper cites.
M. Freitag and Y. Al-Onaizan, “Beam search strategies for neural machine translation,” ACL 2017 , p. 56, 2017
2017
Earlier work this paper cites.
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei, “Deep reinforcement learning from human preferences,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
P. Rajpurkar, R. Jia, and P. Liang, “Know what you don’t know: Unanswerable questions for SQuAD,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Melbourne, Australia: Association for Computational Linguistics, Jul. 2018
2018
Earlier work this paper cites.
B. Wang and N. Z. Gong, “Stealing hyperparameters in machine learning,” in IEEE symposium on security and privacy , 2018, pp. 36–52
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi, “The curious case of neural text degeneration,” in International Conference on Learning Representations , 2019
2019
Earlier work this paper cites.
M. Sap, H. Rashkin, D. Chen, R. Le Bras, and Y. Choi, “Social IQa: Commonsense reasoning about social interactions,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . Hong Kong, China: Association for Computational Linguistics, Nov. 2019
2019
Earlier work this paper cites.
P. Stanchev, W. Wang, and H. Ney, “EED: Extended edit distance measure for machine translation,” in Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1) . Association for Computational Linguistics, 2019
2019
Earlier work this paper cites.
F. Petroni, T. Rocktäschel, S. Riedel, P. Lewis, A. Bakhtin, Y. Wu, and A. Miller, “Language models as knowledge bases?” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) , 2019, pp. 2463–2473
2019
Earlier work this paper cites.
E. Wallace, S. Feng, N. Kandpal, M. Gardner, and S. Singh, “Universal adversarial triggers for attacking and analyzing NLP,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . Hong Kong, China: Association for Computational Linguistics, 2019
2019
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds. Curran Associates, Inc., 2020
2020
Earlier work this paper cites.
T. Linzen, “How can we accelerate progress towards human-like linguistic generalization?” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Association for Computational Linguistics, 2020. [Online]. Available: https://aclanthology.org/2020.acl-main.465
2020
Earlier work this paper cites.
T. Shin, Y. Razeghi, R. L. Logan IV, E. Wallace, and S. Singh, “Autoprompt: Eliciting knowledge from language models with automatically generated prompts,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2020, pp. 4222–4235
2020
Earlier work this paper cites.
B. Wang and A. Komatsuzaki, “GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model,” https://github.com/kingoflolz/mesh-transformer-jax , May 2021
2021
Earlier work this paper cites.
T. Gao, A. Fisch, and D. Chen, “Making pre-trained language models better few-shot learners,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , Aug. 2021
2021
Cited alongside, same era.
B. Lester, R. Al-Rfou, and N. Constant, “The power of scale for parameter-efficient prompt tuning,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021. [Online]. Available: https://aclanthology.org/2021.emnlp-main.243
2021
Cited alongside, same era.
Z. Zhao, E. Wallace, S. Feng, D. Klein, and S. Singh, “Calibrate before use: Improving few-shot performance of language models,” in International Conference on Machine Learning . PMLR, 2021
2021
Cited alongside, same era.
S. M. Xie, A. Raghunathan, P. Liang, and T. Ma, “An explanation of in-context learning as implicit bayesian inference,” in International Conference on Learning Representations , 2021
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
E. Almazrouei, H. Alobeidli, A. Alshamsi, A. Cappelli, R. Cojocaru, M. Debbah, E. Goffinet, D. Heslow, J. Launay, Q. Malartic, B. Noune, B. Pannier, and G. Penedo, “Falcon-40B: an open large language model with state-of-the-art performance,” 2023
2023
Later among the works it cites.
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2021
Cited alongside, same era.
N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song, U. Erlingsson et al. , “Extracting training data from large language models,” in 30th USENIX Security Symposium (USENIX Security 21) , 2021, pp. 2633–2650
2021
Cited alongside, same era.
X. He, J. Jia, M. Backes, N. Z. Gong, and Y. Zhang, “Stealing links from graph neural networks,” in USENIX security symposium , 2021, pp. 2669–2686
2021
Cited alongside, same era.
2022
Cited alongside, same era.
U. Shaham and O. Levy, “What do you get when you cross beam search with nucleus sampling?” Insights 2022 , p. 38, 2022
2022
Cited alongside, same era.
D. Zhou, N. Schärli, L. Hou, J. Wei, N. Scales, X. Wang, D. Schuurmans, C. Cui, O. Bousquet, Q. V. Le et al. , “Least-to-most prompting enables complex reasoning in large language models,” in The Eleventh International Conference on Learning Representations , 2022
2022
Cited alongside, same era.
T. Dettmers, M. Lewis, S. Shleifer, and L. Zettlemoyer, “8-bit optimizers via block-wise quantization,” 9th International Conference on Learning Representations, ICLR , 2022
2022
Cited alongside, same era.
S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. Diab, X. Li, X. V. Lin, T. Mihaylov, M. Ott, S. Shleifer, K. Shuster, D. Simig, P. S. Koura, A. Sridhar, T. Wang, and L. Zettlemoyer, “Opt: Open pre-trained transformer language models,” 2022
2022
Cited alongside, same era.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” Advances in neural information processing systems , vol. 35, pp. 22 199–22 213, 2022
2022
Cited alongside, same era.
N. Jain, A. Schwarzschild, Y. Wen, G. Somepalli, J. Kirchenbauer, P. yeh Chiang, M. Goldblum, A. Saha, J. Geiping, and T. Goldstein, “Baseline defenses for adversarial attacks against aligned language models,” 2023
2023
Later among the works it cites.
X. Liu, Y. Zheng, Z. Du, M. Ding, Y. Qian, Z. Yang, and J. Tang, “Gpt understands, too,” AI Open , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Naseh, K. Krishna, M. Iyyer, and A. Houmansadr, “Stealing the decoding algorithms of language models,” ser. CCS ’23. New York, NY, USA: Association for Computing Machinery, 2023. [Online]. Available: https://doi.org/10.1145/3576915.3616652
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Yang, X. Zhang, Y. Jiang, X. Chen, H. Wang, S. Ji, and Z. Wang, “Prsa: Prompt reverse stealing attacks against large language models,” 2024
2024
Closest in time.
Y. Liu, Y. Jia, R. Geng, J. Jia, and N. Z. Gong, “Formalizing and benchmarking prompt injection attacks and defenses,” in USENIX Security Symposium , 2024
2024
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M. Tang, A. Dai, L. DiValentin, A. Ding, A. Hass, N. Z. Gong, and Y. Chen, “Modelguard: Information-theoretic defense against model extraction attacks,” in USENIX security symposium , 2024
2024
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