Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have attracted significant attention in recent years.
Y. Uchida et al. , “Embedding watermarks into deep neural networks,” in Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval , ser. ICMR ’17. New York, NY, USA: Association for Computing Machinery, 2017, p. 269–277
2017
Earlier work this paper cites.
Y. Adi et al. , “Turning your weakness into a strength: Watermarking deep neural networks by backdooring,” in 27th USENIX Security Symposium, USENIX Security 2018, Baltimore, MD, USA, August 15-17, 2018 , W. Enck and A. P. Felt, Eds. USENIX Association, 2018, pp. 1615–1631
2018
Earlier work this paper cites.
J. Zhang et al. , “Protecting intellectual property of deep neural networks with watermarking,” in Proceedings of the 2018 on Asia Conference on Computer and Communications Security , ser. ASIACCS ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 159–172
2018
Earlier work this paper cites.
J. Ebrahimi et al. , “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
2018
Earlier work this paper cites.
E. Le Merrer et al. , “Adversarial frontier stitching for remote neural network watermarking,” Neural Computing and Applications , vol. 32, no. 13, p. 9233–9244, Aug. 2019
2019
Earlier work this paper cites.
B. D. Rouhani et al. , “Deepsigns: An end-to-end watermarking framework for ownership protection of deep neural networks,” in Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems , ser. ASPLOS ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 485–497
2019
Earlier work this paper cites.
T. Shin et al. , “AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , B. Webber et al. , Eds. Online: Association for Computational Linguistics, Nov. 2020, pp. 4222–4235. [Online]. Available: https://aclanthology.org/2020.emnlp-main.346
2020
Earlier work this paper cites.
T. B. Brown et al. , “Language models are few-shot learners,” in Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual , H. Larochelle et al. , Eds., 2020
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
X. Cao et al. , “Ipguard: Protecting intellectual property of deep neural networks via fingerprinting the classification boundary,” in Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security , ser. ASIA CCS ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 14–25
2021
Earlier work this paper cites.
N. Lukas et al. , “Deep neural network fingerprinting by conferrable adversarial examples,” in 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 . OpenReview.net, 2021
2021
Earlier work this paper cites.
M. Xue et al. , “Dnn intellectual property protection: Taxonomy, attacks and evaluations (invited paper),” in Proceedings of the 2021 on Great Lakes Symposium on VLSI , ser. GLSVLSI ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 455–460
2021
Earlier work this paper cites.
C. Guo et al. , “Gradient-based adversarial attacks against text transformers,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , M.-F. Moens et al. , Eds. Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 5747–5757
2021
Cited alongside, same era.
J. Chen et al. , “Copy, right? a testing framework for copyright protection of deep learning models,” in 2022 IEEE Symposium on Security and Privacy (SP) . San Francisco, CA, USA: IEEE Computer Society, 2022, pp. 824–841
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2023
A. Q. Jiang et al. , “Mistral 7b,” arXiv preprint, arXiv:2310.06825 , 2023
2023
Later among the works it cites.
L. Zheng et al. , “Judging llm-as-a-judge with mt-bench and chatbot arena,” in Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023 , A. Oh et al. , Eds., 2023
2023
Later among the works it cites.
R. Taori et al. , “Stanford alpaca: An instruction-following llama model,” https://github.com/tatsu-lab/stanford_alpaca
2023
Later among the works it cites.
T. Dettmers et al. , “Qlora: Efficient finetuning of quantized llms,” in Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023 , A. Oh et al. , Eds., 2023
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
P. Li et al. , “Plmmark: A secure and robust black-box watermarking framework for pre-trained language models,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 12, pp. 14 991–14 999, Jun. 2023
2023
Cited alongside, same era.
E. Lucas and T. Havens, “GPTs don’t keep secrets: Searching for backdoor watermark triggers in autoregressive language models,” in Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023) , A. Ovalle et al. , Eds. Toronto, Canada: Association for Computational Linguistics, Jul. 2023, pp. 242–248
2023
Cited alongside, same era.
E. Jones et al. , “Automatically auditing large language models via discrete optimization,” in International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA , ser. Proceedings of Machine Learning Research, A. Krause et al. , Eds., vol. 202. PMLR, 2023, pp. 15 307–15 329
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Teknium, “Openhermes 2.5: An open dataset of synthetic data for generalist llm assistants,” 2023. [Online]. Available: https://huggingface.co/datasets/teknium/OpenHermes-2.5
2023
Later among the works it cites.
2023
Later among the works it cites.
2024
Closest in time.
Amazon, “Amazon ec2 p4 instances,” https://aws.amazon.com/ec2/instance-types/p4
2024
Closest in time.
2024
Closest in time.
J. Xu et al. , “Instructional fingerprinting of large language models,” in Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , K. Duh et al. , Eds. Mexico City, Mexico: Association for Computational Linguistics, Jun. 2024, pp. 3277–3306
2024
Closest in time.
Venturebeat, “Mistral CEO confirms ‘leak’ of new open source AI model nearing GPT-4 performance,” https://venturebeat.com/ai/mistral-ceo-confirms-leak-of-new-open-source-ai-model-nearing-gpt-4-performance/, 2024, [Accessed 2024-04-20]
2024
Closest in time.