Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have exhibited remarkable capabilities in text generation tasks.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Similarity estimation techniques from rounding algorithms
Moses Charikar. 2002 · 2002
Earlier work this paper cites.
An introduction to ROC analysis
Tom Fawcett. 2006 · 2006
Earlier work this paper cites.
Linguistic steganography on twitter: hierarchical language modeling with manual interaction
Alex Wilson, Phil Blunsom, and Andrew D. Ker. 2014 · 2014
Earlier work this paper cites.
The enemy in your own camp: How well can we detect statistically-generated fake reviews - an adversarial study
Dirk Hovy. 2016 · 2016
Earlier work this paper cites.
TDNN: A two-stage deep neural network for prompt-independent automated essay scoring
Cancan Jin, Ben He, Kai Hui, and Le Sun. 2018 · 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 · 2019
Earlier work this paper cites.
GLTR: statistical detection and visualization of generated text
Sebastian Gehrmann, Hendrik Strobelt, and Alexander M. Rush. 2019 · 2019
Earlier work this paper cites.
Defending against neural fake news
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019 · 2019
Earlier work this paper cites.
Automatic detection of machine generated text: A critical survey
Ganesh Jawahar, Muhammad Abdul-Mageed, and Laks V. S. Lakshmanan. 2020 · 2020
Earlier work this paper cites.
On the limitations of human-computer agreement in automated essay scoring
Afrizal Doewes and Mykola Pechenizkiy. 2021 · 2021
Earlier work this paper cites.
Unsupervised and distributional detection of machine-generated text
Matthias Gallé, Jos Rozen, Germán Kruszewski, and Hady Elsahar. 2021 · 2021
Earlier work this paper cites.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Y. Zhao, Yanping Huang, Andrew M. Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
Cited alongside, same era.
Machine generated text: A comprehensive survey of threat models and detection methods
Evan Crothers, Nathalie Japkowicz, and Herna L. Viktor. 2022 · 2022
Cited alongside, same era.
Chatgpt: The end of online exam integrity?
Teo Susnjak. 2022 · 2022
Cited alongside, same era.
On the use of bert for automated essay scoring: Joint learning of multi-scale essay representation
Yongjie Wang, Chuang Wang, Ruobing Li, and Hui Lin. 2022 · 2022
Cited alongside, same era.
Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense
Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting, and Mohit Iyyer. 2023 · 2023
Later among the works it cites.
Large language models as counterfactual generator: Strengths and weaknesses
Yongqi Li, Mayi Xu, Xin Miao, Shen Zhou, and Tieyun Qian. 2023 · 2023
Later among the works it cites.
GPT detectors are biased against non-native english writers
Weixin Liang, Mert Yüksekgönül, Yining Mao, Eric Wu, and James Zou. 2023 · 2023
Later among the works it cites.
Large language models can be guided to evade ai-generated text detection
Ning Lu, Shengcai Liu, Rui He, Qi Wang, and Ke Tang. 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…
Wissam Antoun, Virginie Mouilleron, Benoît Sagot, and Djamé Seddah. 2023 · 2023
Cited alongside, same era.
Paraphrase detection: Human vs. machine content
Jonas Becker, Jan Philip Wahle, Terry Ruas, and Bela Gipp. 2023 · 2023
Cited alongside, same era.
On the possibilities of ai-generated text detection
Souradip Chakraborty, Amrit Singh Bedi, Sicheng Zhu, Bang An, Dinesh Manocha, and Furong Huang. 2023 · 2023
Cited alongside, same era.
A pathway towards responsible AI generated content
Chen Chen, Jie Fu, and Lingjuan Lyu. 2023 · 2023
Cited alongside, same era.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023 · 2023
Cited alongside, same era.
How close is chatgpt to human experts? comparison corpus, evaluation, and detection
Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, and Yupeng Wu. 2023 · 2023
Cited alongside, same era.
Audiogpt: Understanding and generating speech, music, sound, and talking head
Rongjie Huang, Mingze Li, Dongchao Yang, Jiatong Shi, Xuankai Chang, Zhenhui Ye, Yuning Wu, Zhiqing Hong, Jiawei Huang, Jinglin Liu, Yi Ren, Zhou Zhao, and Shinji Watanabe. 2023 · 2023
Cited alongside, same era.
A watermark for large language models
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, and Tom Goldstein. 2023 · 2023
Cited alongside, same era.
Fatemehsadat Mireshghallah, Justus Mattern, Sicun Gao, Reza Shokri, and Taylor Berg-Kirkpatrick. 2023 · 2023
Later among the works it cites.
Rabee Qasem, Banan Tantour, and Mohammed Maree. 2023 · 2023
Later among the works it cites.
Can ai-generated text be reliably detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi. 2023 · 2023
Later among the works it cites.
Identifying and extracting rare disease phenotypes with large language models
Cathy Shyr, Yan Hu, Paul A. Harris, and Hua Xu. 2023 · 2023
Later among the works it cites.
Detectllm: Leveraging log rank information for zero-shot detection of machine-generated text
Jinyan Su, Terry Yue Zhuo, Di Wang, and Preslav Nakov. 2023 · 2023
Later among the works it cites.
Ai-generated content (AIGC): A survey
Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, and Hong Lin. 2023 · 2023
Later among the works it cites.
G3detector: General gpt-generated text detector
Haolan Zhan, Xuanli He, Qiongkai Xu, Yuxiang Wu, and Pontus Stenetorp. 2023 · 2023
Later among the works it cites.
Large language models are effective table-to-text generators, evaluators, and feedback providers
Yilun Zhao, Haowei Zhang, Shengyun Si, Linyong Nan, Xiangru Tang, and Arman Cohan. 2023 · 2023
Later among the works it cites.