Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) and Vision-Language Models (VLMs) have made significant advancements in a wide range of natural language processing and vision-language tasks.
Evaluating differentially private machine learning in practice. In 28th USENIX Security Symposium (USENIX Security 19) . 1895–1912
Bargav Jayaraman and David Evans. 2019 · 1912
Earlier work this paper cites.
Asymptotic expansions of integrals
Norman Bleistein and Richard A Handelsman. 1986 · 1986
Earlier work this paper cites.
Analysis of numerical methods
Eugene Isaacson and Herbert Bishop Keller. 2012 · 2012
Earlier work this paper cites.
Asymptotic methods in analysis
Nicolaas Govert De Bruijn. 2014 · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 . Springer, 740–755
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
Earlier work this paper cites.
Sentiment analysis algorithms and applications: A survey
Walaa Medhat, Ahmed Hassan, and Hoda Korashy. 2014 · 2014
Earlier work this paper cites.
From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. 2014 · 2014
Earlier work this paper cites.
Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP) . IEEE, 3–18
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
Earlier work this paper cites.
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes. 2018 · 2018
Earlier work this paper cites.
Privacy risk in machine learning: Analyzing the connection to overfitting. In 2018 IEEE 31st computer security foundations symposium (CSF) . IEEE, 268–282
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018 · 2018
Earlier work this paper cites.
nocaps: novel object captioning at scale. In Proceedings of the IEEE International Conference on Computer Vision . 8948–8957
Harsh Agrawal, Karan Desai, Yufei Wang, Xinlei Chen, Rishabh Jain, Mark Johnson, Dhruv Batra, Devi Parikh, Stefan Lee, and Peter Anderson. 2019 · 2019
Earlier work this paper cites.
A comprehensive survey of deep learning for image captioning
MD Zakir Hossain, Ferdous Sohel, Mohd Fairuz Shiratuddin, and Hamid Laga. 2019 · 2019
Earlier work this paper cites.
Aran Komatsuzaki. 2019 · 2019
Earlier work this paper cites.
Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. In 2019 IEEE symposium on security and privacy (SP) . IEEE, 739–753
Milad Nasr, Reza Shokri, and Amir Houmansadr. 2019 · 2019
Earlier work this paper cites.
Towards VQA Models That Can Read. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 8317–8326
Amanpreet Singh, Vivek Natarjan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. 2019 · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom B Brown. 2020 · 2020
Earlier work this paper cites.
The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 2020
Earlier work this paper cites.
Scaling laws for autoregressive generative modeling
Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B Brown, Prafulla Dhariwal, Scott Gray, et al · 2020
Cited alongside, same era.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Cited alongside, same era.
Neural machine translation: A review
Felix Stahlberg. 2020 · 2020
Cited alongside, same era.
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman. 2021 · 2021
Membership inference attacks against language models via neighbourhood comparison
Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schölkopf, Mrinmaya Sachan, and Taylor Berg-Kirkpatrick. 2023 · 2023
Later among the works it cites.
Detecting pretraining data from large language models
Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer. 2023 · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 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.
Extracting training data from large language models. In 30th USENIX Security Symposium (USENIX Security 21) . 2633–2650
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
Cited alongside, same era.
Automatic text summarization: A comprehensive survey
Wafaa S El-Kassas, Cherif R Salama, Ahmed A Rafea, and Hoda K Mohamed. 2021 · 2021
Cited alongside, same era.
Dataset inference: Ownership resolution in machine learning
Pratyush Maini, Mohammad Yaghini, and Nicolas Papernot. 2021 · 2021
Cited alongside, same era.
On the importance of difficulty calibration in membership inference attacks
Lauren Watson, Chuan Guo, Graham Cormode, and Alex Sablayrolles. 2021 · 2021
Cited alongside, same era.
Deduplicating training data mitigates privacy risks in language models. In International Conference on Machine Learning . PMLR, 10697–10707
Nikhil Kandpal, Eric Wallace, and Colin Raffel. 2022 · 2022
Cited alongside, same era.
Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Cited alongside, same era.
Cogvlm: Visual expert for pretrained language models
Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Xixuan Song, et al · 2023
Later among the works it cites.
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
Later among the works it cites.
Hello GPT-4o
2024 · 2024
Closest in time.
Do membership inference attacks work on large language models?
Michael Duan, Anshuman Suri, Niloofar Mireshghallah, Sewon Min, Weijia Shi, Luke Zettlemoyer, Yulia Tsvetkov, Yejin Choi, David Evans, and Hannaneh Hajishirzi. 2024 · 2024
Closest in time.
Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2024a · 2024
Closest in time.
Rethinking machine unlearning for large language models
Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Xiaojun Xu, Yuguang Yao, Hang Li, Kush R Varshney, et al · 2024
Closest in time.
LLM Dataset Inference: Did you train on my dataset?
Pratyush Maini, Hengrui Jia, Nicolas Papernot, and Adam Dziedzic. 2024 · 2024
Closest in time.
The fineweb datasets: Decanting the web for the finest text data at scale
Guilherme Penedo, Hynek Kydlíček, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, Thomas Wolf, et al · 2024
Closest in time.
Copyright protection in generative ai: A technical perspective
Jie Ren, Han Xu, Pengfei He, Yingqian Cui, Shenglai Zeng, Jiankun Zhang, Hongzhi Wen, Jiayuan Ding, Hui Liu, Yi Chang, et al · 2024
Closest in time.
Gemma: Open models based on gemini research and technology
Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al · 2024
Closest in time.
Gemma 2: Improving open language models at a practical size
Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al · 2024
Closest in time.
Proving membership in LLM pretraining data via data watermarks
Johnny Tian-Zheng Wei, Ryan Yixiang Wang, and Robin Jia. 2024 · 2024
Closest in time.
ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods
Roy Xie, Junlin Wang, Ruomin Huang, Minxing Zhang, Rong Ge, Jian Pei, Neil Zhenqiang Gong, and Bhuwan Dhingra. 2024 · 2024
Closest in time.
Demystifying membership inference attacks in machine learning as a service
Stacey Truex, Ling Liu, Mehmet Emre Gursoy, Lei Yu, and Wenqi Wei. 2019 · 2089
Closest in time.