Fetching the paper…
Reading the bibliography…
Protecting the copyright of large language models (LLMs) has become crucial due to their resource-intensive training and accompanying carefully designed licenses.
Blackmarks: Blackbox multibit watermarking for deep neural networks
Chen, H., Rouhani, B. D., and Koushanfar, F · 1904
Earlier work this paper cites.
Constant-size commitments to polynomials and their applications
Kate, A., Zaverucha, G. M., and Goldberg, I · 2010
Earlier work this paper cites.
Snarks for c: Verifying program executions succinctly and in zero knowledge
Ben-Sasson, E., Chiesa, A., Genkin, D., Tromer, E., and Virza, M · 2013
Earlier work this paper cites.
Race: Large-scale reading comprehension dataset from examinations
Lai, G., Xie, Q., Liu, H., Yang, Y., and Hovy, E · 2017
Earlier work this paper cites.
Embedding watermarks into deep neural networks
Uchida, Y., Nagai, Y., Sakazawa, S., and Satoh, S · 2017
Earlier work this paper cites.
Think you have solved question answering? try arc, the ai2 reasoning challenge
Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., and Tafjord, O · 2018
Earlier work this paper cites.
Watermarking deep neural networks for embedded systems
Guo, J. and Potkonjak, M · 2018
Earlier work this paper cites.
Doubly-efficient zksnarks without trusted setup
Wahby, R. S., Tzialla, I., Shelat, A., Thaler, J., and Walfish, M · 2018
Earlier work this paper cites.
The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
Earlier work this paper cites.
Deepmarks: A secure fingerprinting framework for digital rights management of deep learning models
Chen, H., Rouhani, B. D., Fu, C., Zhao, J., and Koushanfar, F · 2019
Earlier work this paper cites.
Boolq: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
Earlier work this paper cites.
Rethinking deep neural network ownership verification: Embedding passports to defeat ambiguity attacks
Fan, L., Ng, K. W., and Chan, C. S · 2019
Earlier work this paper cites.
The knowledge complexity of interactive proof-systems
Goldwasser, S., Micali, S., and Rackoff, C · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
Earlier work this paper cites.
Deep neural network fingerprinting by conferrable adversarial examples
Lukas, N., Zhang, Y., and Kerschbaum, F · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Earlier work this paper cites.
Deepsigns: an end-to-end watermarking framework for protecting the ownership of deep neural networks
Rouhani, B. D., Chen, H., and Koushanfar, F · 2019
Earlier work this paper cites.
Attacks on digital watermarks for deep neural networks
Wang, T. and Kerschbaum, F · 2019
Earlier work this paper cites.
Hellaswag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y · 2019
Earlier work this paper cites.
Piqa: Reasoning about physical commonsense in natural language
Bisk, Y., Zellers, R., Gao, J., Choi, Y., et al · 2020
Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Earlier work this paper cites.
Marlin: Preprocessing zksnarks with universal and updatable SRS
Chiesa, A., Hu, Y., Maller, M., Mishra, P., Vesely, P., and Ward, N. P · 2020
Earlier work this paper cites.
Stargan v2: Diverse image synthesis for multiple domains
Choi, Y., Uh, Y., Yoo, J., and Ha, J.-W · 2020
Earlier work this paper cites.
The pile: An 800gb dataset of diverse text for language modeling
Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., et al · 2020
Earlier work this paper cites.
Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2020
Earlier work this paper cites.
Analyzing and improving the image quality of stylegan
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T · 2020
Earlier work this paper cites.
Adversarial frontier stitching for remote neural network watermarking
Le Merrer, E., Perez, P., and Trédan, G · 2020
Earlier work this paper cites.
Watermarking in deep neural networks via error back-propagation
Wang, J., Wu, H., Zhang, X., and Yao, Y · 2020
Earlier work this paper cites.
Watermarking neural networks with watermarked images
Wu, H., Liu, G., Yao, Y., and Zhang, X · 2020
Earlier work this paper cites.
Afa: Adversarial fingerprinting authentication for deep neural networks
Zhao, J., Hu, Q., Liu, G., Ma, X., Chen, F., and Hassan, M. M · 2020
Earlier work this paper cites.
Adversarial watermarking transformer: Towards tracing text provenance with data hiding
Abdelnabi, S. and Fritz, M · 2021
Cited alongside, same era.
A systematic review on model watermarking for neural networks
Boenisch, F · 2021
Cited alongside, same era.
You are caught stealing my winning lottery ticket! making a lottery ticket claim its ownership
Chen, X., Chen, T., Zhang, Z., and Wang, Z · 2021
Cited alongside, same era.
Deepipr: Deep neural network intellectual property protection with passports
Fan, L., Ng, K. W., Chan, C. S., and Yang, Q · 2021
Cited alongside, same era.
Proof-of-learning: Definitions and practice
Jia, H., Yaghini, M., Choquette-Choo, C. A., Dullerud, N., Thudi, A., Chandrasekaran, V., and Papernot, N · 2021
Cited alongside, same era.
Watermarking deep neural networks with greedy residuals
Liu, H., Weng, Z., and Zhu, Y · 2021
https://github.com/baichuan-inc/Baichuan-7B , 2023
BaiChuan-Inc · 2023
Closest in time.
Pythia: A suite for analyzing large language models across training and scaling
Biderman, S., Schoelkopf, H., Anthony, Q. G., Bradley, H., O’Brien, K., Hallahan, E., Khan, M. A., Purohit, S., Prashanth, U. S., Raff, E., et al · 2023
Closest in time.
Undetectable watermarks for language models
Christ, M., Gunn, S., and Zamir, O · 2023
Closest in time.
Redpajama: An open source recipe to reproduce llama training dataset, 2023
Computer, T · 2023
Closest in time.
Free dolly: Introducing the world’s first truly open instruction-tuned llm, 2023
Conover, M., Hayes, M., Mathur, A., Xie, J., Wan, J., Shah, S., Ghodsi, A., Wendell, P., Zaharia, M., and Xin, R · 2023
Closest in time.
Efficient and effective text encoding for chinese llama and alpaca
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Winogrande: An adversarial winograd schema challenge at scale
Sakaguchi, K., Bras, R. L., Bhagavatula, C., and Choi, Y · 2021
Cited alongside, same era.
Riga: Covert and robust white-box watermarking of deep neural networks
Wang, T. and Kerschbaum, F · 2021
Cited alongside, same era.
Protecting your nlg models with semantic and robust watermarks
Xiang, T., Xie, C., Guo, S., Li, J., and Zhang, T · 2021
Cited alongside, same era.
Robust black-box watermarking for deep neural network using inverse document frequency
Yadollahi, M. M., Shoeleh, F., Dadkhah, S., and Ghorbani, A. A · 2021
Cited alongside, same era.
Training a helpful and harmless assistant with reinforcement learning from human feedback
Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., et al · 2022
Cited alongside, same era.
Gpt-neox-20b: An open-source autoregressive language model
Black, S., Biderman, S., Hallahan, E., Anthony, Q., Gao, L., Golding, L., He, H., Leahy, C., McDonell, K., Phang, J., et al · 2022
Cited alongside, same era.
Cui, Y., Yang, Z., and Yao, X · 2023
Closest in time.
Qlora: Efficient finetuning of quantized llms
Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L · 2023
Closest in time.
Cerebras-gpt: Open compute-optimal language models trained on the cerebras wafer-scale cluster
Dey, N., Gosal, G., Khachane, H., Marshall, W., Pathria, R., Tom, M., Hestness, J., et al · 2023
Closest in time.
Easylm: A simple and scalable training framework for large language models, 2023
Geng, X · 2023
Closest in time.
Openllama: An open reproduction of llama, May 2023
Geng, X. and Liu, H · 2023
Closest in time.
GPT-4, O · 2023
Closest in time.
Medalpaca–an open-source collection of medical conversational ai models and training data
Han, T., Adams, L. C., Papaioannou, J.-M., Grundmann, P., Oberhauser, T., Löser, A., Truhn, D., and Bressem, K. K · 2023
Closest in time.
A watermark for large language models
Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., and Goldstein, T · 2023
Closest in time.
Openassistant conversations–democratizing large language model alignment
Köpf, A., Kilcher, Y., von Rütte, D., Anagnostidis, S., Tam, Z.-R., Stevens, K., Barhoum, A., Duc, N. M., Stanley, O., Nagyfi, R., et al · 2023
Closest in time.
Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense
Krishna, K., Song, Y., Karpinska, M., Wieting, J., and Iyyer, M · 2023
Closest in time.
Origin tracing and detecting of llms
Li, L., Wang, P., Ren, K., Sun, T., and Qiu, X · 2023
Closest in time.
Billa: A bilingual llama with enhanced reasoning ability
Li, Z · 2023
Closest in time.
Detectgpt: Zero-shot machine-generated text detection using probability curvature
Mitchell, E., Lee, Y., Khazatsky, A., Manning, C. D., and Finn, C · 2023
Closest in time.
Ai classifier
OpenAI · 2023
Closest in time.
Penedo, G., Malartic, Q., Hesslow, D., Cojocaru, R., Cappelli, A., Alobeidli, H., Pannier, B., Almazrouei, E., and Launay, J · 2023
Closest in time.
Can ai-generated text be reliably detected?
Sadasivan, V. S., Kumar, A., Balasubramanian, S., Wang, W., and Feizi, S · 2023
Closest in time.
Stanford alpaca: An instruction-following llama model
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
Closest in time.
Internlm: A multilingual language model with progressively enhanced capabilities
Team, I · 2023
Closest in time.
Gptzero: An ai text detector
Tian, E · 2023
Closest in time.
https://github.com/tloen/alpaca-lora , 2023
Wang, E. J · 2023
Closest in time.
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Zhu, D., Chen, J., Shen, X., Li, X., and Elhoseiny, M · 2023
Closest in time.
Trap: Targeted random adversarial prompt honeypot for black-box identification
Gubri, M., Ulmer, D., Lee, H., Yun, S., and Oh, S. J · 2024
Closest in time.
zkllm: Zero knowledge proofs for large language models
Sun, H., Li, J., and Zhang, H · 2024
Closest in time.
Instructional fingerprinting of large language models
Xu, J., Wang, F., Ma, M. D., Koh, P. W., Xiao, C., and Chen, M · 2024
Closest in time.