Fetching the paper…
Reading the bibliography…
While large language models (LLMs) exhibit significant utility across various domains, they simultaneously are susceptible to exploitation for unethical purposes, including academic misconduct and dissemination of misinformation.
Machine-generated multimedia content
Nathan Nichols and Kristian Hammond · 2009
Earlier work this paper cites.
Duplicate and fake publications in the scientific literature: how many scigen papers in computer science?
Cyril Labbé and Dominique Labbé · 2013
Earlier work this paper cites.
Data augmentation for low-resource neural machine translation
Marzieh Fadaee, Arianna Bisazza, and Christof Monz · 2017
Earlier work this paper cites.
John Wieting and Kevin Gimpel · 2017
Earlier work this paper cites.
Universal sentence encoder for english
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, et al · 2018
Earlier work this paper cites.
Taku Kudo and John Richardson · 2018
Earlier work this paper cites.
Gltr: Statistical detection and visualization of generated text
Sebastian Gehrmann, Hendrik Strobelt, and Alexander M Rush · 2019
Earlier work this paper cites.
On the use of arxiv as a dataset
Colin B Clement, Matthew Bierbaum, Kevin P O’Keeffe, and Alexander A Alemi · 2019
Earlier work this paper cites.
Eli5: Long form question answering
Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli · 2019
Earlier work this paper cites.
Automatic detection of generated text is easiest when humans are fooled
Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, and Douglas Eck · 2019
Earlier work this paper cites.
Improving massively multilingual neural machine translation and zero-shot translation
Biao Zhang, Philip Williams, Ivan Titov, and Rico Sennrich · 2020
Earlier work this paper cites.
All that’s ‘human’is not gold: Evaluating human evaluation of generated text
Elizabeth Clark, Tal August, Sofia Serrano, Nikita Haduong, Suchin Gururangan, and Noah A Smith · 2021
Earlier work this paper cites.
Turingbench: A benchmark environment for turing test in the age of neural text generation
Adaku Uchendu, Zeyu Ma, Thai Le, Rui Zhang, and Dongwon Lee · 2021
Earlier work this paper cites.
Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski, Noah A Smith, and Yejin Choi · 2021
Earlier work this paper cites.
Paraphrastic representations at scale
John Wieting, Kevin Gimpel, Graham Neubig, and Taylor Berg-Kirkpatrick · 2021
Earlier work this paper cites.
Creating and detecting fake reviews of online products
Joni Salminen, Chandrashekhar Kandpal, Ahmed Mohamed Kamel, Soon-gyo Jung, and Bernard J Jansen · 2022
Earlier work this paper cites.
Adversarial robustness of neural-statistical features in detection of generative transformers
Evan Crothers, Nathalie Japkowicz, Herna Viktor, and Paula Branco · 2022
Earlier work this paper cites.
Exploring document-level literary machine translation with parallel paragraphs from world literature
Katherine Thai, Marzena Karpinska, Kalpesh Krishna, Bill Ray, Moira Inghilleri, John Wieting, and Mohit Iyyer · 2022
Earlier work this paper cites.
In-context learning for text classification with many labels
Aristides Milios, Siva Reddy, and Dzmitry Bahdanau · 2023
Earlier work this paper cites.
Chatgpt and large language model (llm) chatbots: The current state of acceptability and a proposal for guidelines on utilization in academic medicine
Jin K Kim, Michael Chua, Mandy Rickard, and Armando Lorenzo · 2023
Earlier work this paper cites.
Fake news detectors are biased against texts generated by large language models
Jinyan Su, Terry Yue Zhuo, Jonibek Mansurov, Di Wang, and Preslav Nakov · 2023
Earlier work this paper cites.
The looming threat of fake and llm-generated linkedin profiles: Challenges and opportunities for detection and prevention
Navid Ayoobi, Sadat Shahriar, and Arjun Mukherjee · 2023
Earlier work this paper cites.
Combating misinformation in the age of llms: Opportunities and challenges
Canyu Chen and Kai Shu · 2023
Cited alongside, same era.
Towards mitigating llm hallucination via self reflection
Ziwei Ji, Tiezheng Yu, Yan Xu, Nayeon Lee, Etsuko Ishii, and Pascale Fung · 2023
Cited alongside, same era.
Llm lies: Hallucinations are not bugs, but features as adversarial examples
Jia-Yu Yao, Kun-Peng Ning, Zhen-Hui Liu, Mu-Nan Ning, and Li Yuan · 2023
Cited alongside, same era.
Game of tones: faculty detection of gpt-4 generated content in university assessments
Mike Perkins, Jasper Roe, Darius Postma, James McGaughran, and Don Hickerson · 2023
Cited alongside, same era.
Evade chatgpt detectors via a single space
Shuyang Cai and Wanyun Cui · 2023
Later among the works it cites.
Improving llm-based machine translation with systematic self-correction
Zhaopeng Feng, Yan Zhang, Hao Li, Wenqiang Liu, Jun Lang, Yang Feng, Jian Wu, and Zuozhu Liu · 2024
Closest in time.
A systematic survey of text summarization: From statistical methods to large language models
Haopeng Zhang, Philip S Yu, and Jiawei Zhang · 2024
Closest in time.
Student perspectives on using a large language model (llm) for an assignment on professional ethics
Virginia Grande, Natalie Kiesler, and María Andreína Francisco R · 2024
Closest in time.
Beyond binary: Towards embracing complexities in cyberbullying detection and intervention-a position paper
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mayank Soni and Vincent Wade · 2023
Cited alongside, same era.
Beat llms at their own game: Zero-shot llm-generated text detection via querying chatgpt
Biru Zhu, Lifan Yuan, Ganqu Cui, Yangyi Chen, Chong Fu, Bingxiang He, Yangdong Deng, Zhiyuan Liu, Maosong Sun, and Ming Gu · 2023
Cited alongside, same era.
Can ai-generated text be reliably detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi · 2023
Cited alongside, same era.
A survey on llm-gernerated text detection: Necessity, methods, and future directions
Junchao Wu, Shu Yang, Runzhe Zhan, Yulin Yuan, Derek F Wong, and Lidia S Chao · 2023
Cited alongside, same era.
Radar: Robust ai-text detection via adversarial learning
Xiaomeng Hu, Pin-Yu Chen, and Tsung-Yi Ho · 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
Cited alongside, same era.
On the reliability of watermarks for large language models
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, and Tom Goldstein · 2023
Cited alongside, same era.
Semstamp: A semantic watermark with paraphrastic robustness for text generation
Abe Bohan Hou, Jingyu Zhang, Tianxing He, Yichen Wang, Yung-Sung Chuang, Hongwei Wang, Lingfeng Shen, Benjamin Van Durme, Daniel Khashabi, and Yulia Tsvetkov · 2023
Cited alongside, same era.
Kanishk Verma, Kolawole John Adebayo, Joachim Wagner, Megan Reynolds, Rebecca Umbach, Tijana Milosevic, and Brian Davis · 2024
Closest in time.
Seeing through ai’s lens: Enhancing human skepticism towards llm-generated fake news
Navid Ayoobi, Sadat Shahriar, and Arjun Mukherjee · 2024
Closest in time.
Contrasting linguistic patterns in human and llm-generated news text
Alberto Muñoz-Ortiz, Carlos Gómez-Rodríguez, and David Vilares · 2024
Closest in time.
Georgios P Georgiou · 2024
Closest in time.
Mazal Bethany, Brandon Wherry, Emet Bethany, Nishant Vishwamitra, and Peyman Najafirad · 2024
Closest in time.
Technical report on the pangram ai-generated text classifier
Bradley Emi and Max Spero · 2024
Closest in time.
Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense
Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting, and Mohit Iyyer · 2024
Closest in time.
Phi-3 technical report: A highly capable language model locally on your phone
Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, et al · 2024
Closest in time.
Yi: Open foundation models by 01. ai
Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, et al · 2024
Closest in time.
The llama 3 herd of models, 2024
Abhimanyu Dubey et al · 2024
Closest in time.
https://gptzero.me/ , August 2024
GPTZero · 2024
Closest in time.
https://www.zerogpt.com/ , August 2024
ZeroGPT · 2024
Closest in time.
Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, et al · 2024
Closest in time.
Guanghua Li, Wensheng Lu, Wei Zhang, Defu Lian, Kezhong Lu, Rui Mao, Kai Shu, and Hao Liao · 2024
Closest in time.
Fighting fire with fire: can chatgpt detect ai-generated text?
Amrita Bhattacharjee and Huan Liu · 2024
Closest in time.
Watermark stealing in large language models
Nikola Jovanović, Robin Staab, and Martin Vechev · 2024
Closest in time.
Bridging language and items for retrieval and recommendation
Yupeng Hou, Jiacheng Li, Zhankui He, An Yan, Xiusi Chen, and Julian McAuley · 2024
Closest in time.