Fetching the paper…
Reading the bibliography…
The Machine Learning as a Service (MLaaS) market is rapidly expanding and becoming more mature.
WordNet: A Lexical Database for English
George A. Miller · 1995
Earlier work this paper cites.
ROUGE: A Package for Automatic Evaluation of Summaries
Chin-Yew Lin · 2004
Earlier work this paper cites.
Learning Word Vectors for Sentiment Analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
Earlier work this paper cites.
Poisoning Attacks against Support Vector Machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
Earlier work this paper cites.
Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
Earlier work this paper cites.
Character-level Convolutional Networks for Text Classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
Earlier work this paper cites.
SQuAD: 100, 000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
Earlier work this paper cites.
Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2018
Earlier work this paper cites.
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li · 2018
Earlier work this paper cites.
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata · 2018
Earlier work this paper cites.
Know What You Don’t Know: Unanswerable Questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang · 2018
Earlier work this paper cites.
Data Poisoning Attack against Unsupervised Node Embedding Methods
Mingjie Sun, Jian Tang, Huichen Li, Bo Li, Chaowei Xiao, Yao Chen, and Dawn Song · 2018
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Earlier work this paper cites.
Adversarial Reprogramming of Neural Networks
Gamaleldin F. Elsayed, Ian J. Goodfellow, and Jascha Sohl-Dickstein · 2019
Cited alongside, same era.
TextBugger: Generating Adversarial Text Against Real-world Applications
Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, and Ting Wang · 2019
Cited alongside, same era.
Language Models are Unsupervised Multitask Learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Cited alongside, same era.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Nils Reimers and Iryna Gurevych · 2019
Cited alongside, same era.
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2019
Cited alongside, same era.
HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Large Language Models are Zero-Shot Reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
Later among the works it cites.
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2022
Later among the works it cites.
Get a Model! Model Hijacking Attack Against Machine Learning Models
Ahmed Salem, Michael Backes, and Yang Zhang · 2022
Later among the works it cites.
Finetuned Language Models are Zero-Shot Learners
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le · 2022
Later among the works it cites.
A Categorical Archive of ChatGPT Failures
Ali Borji · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew · 2019
Cited alongside, same era.
Language Models are Few-Shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply
Lingjiao Chen, Matei Zaharia, and James Y. Zou · 2020
Cited alongside, same era.
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits · 2020
Cited alongside, same era.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2020
Cited alongside, same era.
BadNL: Backdoor Attacks Against NLP Models with Semantic-preserving Improvements
Xiaoyi Chen, Ahmed Salem, Michael Backes, Shiqing Ma, Qingni Shen, Zhonghai Wu, and Yang Zhang · 2021
Cited alongside, same era.
WARP: Word-level Adversarial ReProgramming
Karen Hambardzumyan, Hrant Khachatrian, and Jonathan May · 2021
Cited alongside, same era.
Lingjiao Chen, Matei Zaharia, and James Zou · 2023
Closest in time.
Toxicity in ChatGPT: Analyzing Persona-assigned Language Models
Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, and Karthik Narasimhan · 2023
Closest in time.
Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz · 2023
Closest in time.
Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks
Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, and Tatsunori Hashimoto · 2023
Closest in time.
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
Potsawee Manakul, Adian Liusie, and Mark J. F. Gales · 2023
Closest in time.
Two-in-One: A Model Hijacking Attack Against Text Generation Models
Wai Man Si, Michael Backes, Yang Zhang, and Ahmed Salem · 2023
Closest in time.
An Analysis of the Automatic Bug Fixing Performance of ChatGPT
Dominik Sobania, Martin Briesch, Carol Hanna, and Justyna Petke · 2023
Closest in time.
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, Binxing Jiao, Yue Zhang, and Xing Xie · 2023
Closest in time.