Fetching the paper…
Reading the bibliography…
With ChatGPT under the spotlight, utilizing large language models (LLMs) to assist academic writing has drawn a significant amount of debate in the community.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
A value for n-person games
LS SHAPLEY. 1953 · 1953
Earlier work this paper cites.
A computer readability formula designed for machine scoring
Meri Coleman and Ta Lin Liau. 1975 · 1975
Earlier work this paper cites.
Variation across speech and writing
Douglas Biber. 1991 · 1991
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, and Pascal Vincent. 2000 · 2000
Earlier work this paper cites.
Teaching academic ESL writing: Practical techniques in vocabulary and grammar
Eli Hinkel. 2003 · 2003
Earlier work this paper cites.
Stance and engagement: A model of interaction in academic discourse
Ken Hyland. 2005 · 2005
Earlier work this paper cites.
Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering. In Proc. 23rd International Conference on Machine learning (ICML’06) . ACM Press, 377–384
Derek Greene and Pádraig Cunningham. 2006 · 2006
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Challenging stereotypes about academic writing: Complexity, elaboration, explicitness
Douglas Biber and Bethany Gray. 2010 · 2010
Earlier work this paper cites.
The Hewlett Foundation: Automated Essay Scoring
The Hewlett Foundation. 2012 · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
LSTM neural networks for language modeling. In Thirteenth annual conference of the international speech communication association
Martin Sundermeyer, Ralf Schlüter, and Hermann Ney. 2012 · 2012
Earlier work this paper cites.
Min Lin, Qiang Chen, and Shuicheng Yan. 2013 · 2013
Earlier work this paper cites.
Grammar for academic writing
Tony Lynch and Kenneth Anderson. 2013 · 2013
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation. In EMNLP
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
DBpedia abstracts: a large-scale, open, multilingual NLP training corpus. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16) . 3339–3343
Martin Brümmer, Milan Dojchinovski, and Sebastian Hellmann. 2016 · 2016
Earlier work this paper cites.
Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems. In 2016 IEEE symposium on security and privacy (SP) . IEEE, 598–617
Anupam Datta, Shayak Sen, and Yair Zick. 2016 · 2016
Earlier work this paper cites.
The rise of social bots
Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. 2016 · 2016
Earlier work this paper cites.
SQuAD: 100,000+ Questions for Machine Comprehension of Text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing . 2383–2392
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Earlier work this paper cites.
SGDR: Stochastic Gradient Descent with Warm Restarts. In International Conference on Learning Representations
Ilya Loshchilov and Frank Hutter. 2017 · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks. In International conference on machine learning . PMLR, 3319–3328
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
Earlier work this paper cites.
Comparative study of CNN and RNN for natural language processing
Wenpeng Yin, Katharina Kann, Mo Yu, and Hinrich Schütze. 2017 · 2017
Earlier work this paper cites.
NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs. In Proceedings of the 12th International Workshop on Semantic Evaluation . Association for Computational Linguistics, New Orleans, Louisiana, 613–621
Christos Baziotis, Athanasiou Nikolaos, Pinelopi Papalampidi, Athanasia Kolovou, Georgios Paraskevopoulos, Nikolaos Ellinas, and Alexandros Potamianos. 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. 2018 · 2018
Earlier work this paper cites.
The narrativeqa reading comprehension challenge
Tomáš Kočiskỳ, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2018 · 2018
Earlier work this paper cites.
Deep Contextualized Word Representations. In NAACL-HLT . ACL, 2227–2237
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Earlier work this paper cites.
Real or fake? learning to discriminate machine from human generated text
Anton Bakhtin, Sam Gross, Myle Ott, Yuntian Deng, Marc’Aurelio Ranzato, and Arthur Szlam. 2019 · 2019
Earlier work this paper cites.
An empirical study on pre-trained embeddings and language models for bot detection. In Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019) . 148–155
Andres Garcia-Silva, Cristian Berrio, and José Manuel Gómez-Pérez. 2019 · 2019
Earlier work this paper cites.
GLTR: Statistical Detection and Visualization of Generated Text. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations . 111–116
Sebastian Gehrmann, Hendrik Strobelt, and Alexander M Rush. 2019 · 2019
Earlier work this paper cites.
Robust fake news detection over time and attack
Benjamin D Horne, Jeppe Nørregaard, and Sibel Adali. 2019 · 2019
Earlier work this paper cites.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 2019
Earlier work this paper cites.
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 · 2019
Earlier work this paper cites.
Decoupled Weight Decay Regularization. In International Conference on Learning Representations
Ilya Loshchilov and Frank Hutter. 2019 · 2019
Earlier work this paper cites.
roberta-base-openai-detector
openai community. 2019 · 2019
Cited alongside, same era.
fairseq: A Fast, Extensible Toolkit for Sequence Modeling. In Proceedings of NAACL-HLT 2019: Demonstrations
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 2019
Cited alongside, same era.
Release strategies and the social impacts of language models
Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Radford, Gretchen Krueger, Jong Wook Kim, Sarah Kreps, et al · 2019
Sentence embeddings using Siamese RoBERTa-networks
Mohammed Abu El-Nasr. 2023 · 2023
Closest in time.
What ChatGPT means for universities: Perceptions of scholars and students
Mehmet Firat. 2023 · 2023
Closest in time.
Nonhuman “authors” and implications for the integrity of scientific publication and medical knowledge
Annette Flanagin, Kirsten Bibbins-Domingo, Michael Berkwits, and Stacy L Christiansen. 2023 · 2023
Closest in time.
The capacity for moral self-correction in large language models
Deep Ganguli, Amanda Askell, Nicholas Schiefer, Thomas Liao, Kamilė Lukošiūtė, Anna Chen, Anna Goldie, Azalia Mirhoseini, Catherine Olsson, Danny Hernandez, et al · 2023
Closest in time.
Meta-Prompt: A Simple Self-Improving Language Agent
Noah Goodman. 2023 · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Defending against neural fake news
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
A decade of social bot detection
Stefano Cresci. 2020 · 2020
Cited alongside, same era.
Are you human? Detecting bots on Twitter Using BERT. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) . IEEE, 631–636
David Dukić, Dominik Keča, and Dominik Stipić. 2020 · 2020
Cited alongside, same era.
Using bert to extract topic-independent sentiment features for social media bot detection. In 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) . IEEE, 0542–0547
Maryam Heidari and James H Jones. 2020 · 2020
Cited alongside, same era.
Automatic Detection of Generated Text is Easiest when Humans are Fooled. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . 1808–1822
Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, and Douglas Eck. 2020 · 2020
Cited alongside, same era.
Captum: A unified and generic model interpretability library for pytorch
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, et al · 2020
Cited alongside, same era.
Finetuning RoBERTa on a custom classification task
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2020 · 2020
Cited alongside, same era.
Electric Sheep on the Pastures of Disinformation and Targeted Phishing Campaigns: The Security Implications of ChatGPT
Kacper T Gradonm. 2023 · 2023
Closest in time.
Social Engineering with chatgpt. In 2023 22nd International Symposium INFOTEH-JAHORINA (INFOTEH) . IEEE, 1–5
Dijana Vukovic Grbic and Igor Dujlovic. 2023 · 2023
Closest in time.
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
Closest in time.
Large language models can be used to effectively scale spear phishing campaigns
Julian Hazell. 2023 · 2023
Closest in time.
Mgtbench: Benchmarking machine-generated text detection
Xinlei He, Xinyue Shen, Zeyuan Chen, Michael Backes, and Yang Zhang. 2023 · 2023
Closest in time.
Using ChatGPT to Fight Misinformation: ChatGPT Nails 72% of 12,000 Verified Claims
Emma Hoes, Sacha Altay, and Juan Bermeo. 2023 · 2023
Closest in time.
Instruction induction: From few examples to natural language task descriptions
Or Honovich, Uri Shaham, Samuel R Bowman, and Omer Levy. 2023 · 2023
Closest in time.
Smart ChatGPT Prompts
Ashish Jaiswal. 2023 · 2023
Closest in time.
Will ChatGPT get you caught? Rethinking of plagiarism detection
Mohammad Khalil and Erkan Er. 2023 · 2023
Closest in time.
DEMASQ: Unmasking the ChatGPT Wordsmith
Kavita Kumari, Alessandro Pegoraro, Hossein Fereidooni, and Ahmad-Reza Sadeghi. 2023 · 2023
Closest in time.
Multi-step Jailbreaking Privacy Attacks on ChatGPT
Haoran Li, Dadi Guo, Wei Fan, Mingshi Xu, and Yangqiu Song. 2023 · 2023
Closest in time.
Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study
Yi Liu, Gelei Deng, Zhengzi Xu, Yuekang Li, Yaowen Zheng, Ying Zhang, Lida Zhao, Tianwei Zhang, and Yang Liu. 2023a · 2023
Closest in time.
ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models
Yikang Liu, Ziyin Zhang, Wanyang Zhang, Shisen Yue, Xiaojing Zhao, Xinyuan Cheng, Yiwen Zhang, and Hai Hu. 2023b · 2023
Closest in time.
MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing . 9960–9987
Dominik Macko, Robert Moro, Adaku Uchendu, Jason Lucas, Michiharu Yamashita, Matúš Pikuliak, Ivan Srba, Thai Le, Dongwon Lee, Jakub Simko, et al · 2023
Closest in time.
Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2023
Closest in time.
Kamil Malinka, Martin Perešíni, Anton Firc, Ondřej Hujňák, and Filip Januš. 2023 · 2023
Closest in time.
Weaponising ChatGPT
Steve Mansfield-Devine. 2023 · 2023
Closest in time.
Detectgpt: Zero-shot machine-generated text detection using probability curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D Manning, and Chelsea Finn. 2023 · 2023
Closest in time.
Awesome ChatGPT Prompts
PlexPt. 2023 · 2023
Closest in time.
From ChatGPT to HackGPT: Meeting the Cybersecurity Threat of Generative AI
Karen Renaud, Merrill Warkentin, and George Westerman. 2023 · 2023
Closest in time.
Generating Phishing Attacks using ChatGPT
Sayak Saha Roy, Krishna Vamsi Naragam, and Shirin Nilizadeh. 2023 · 2023
Closest in time.
ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. In Healthcare , Vol. 11. MDPI, 887
Malik Sallam. 2023 · 2023
Closest in time.
chatgpt-prompt-gen.txt
solrevdev. 2023 · 2023
Closest in time.
Use of ChatGPT generated text for content on Stack Overflow is temporarily banned
StackOverflow. 2023 · 2023
Closest in time.
ChatGPT listed as author on research papers: many scientists disapprove
Chris Stokel-Walker. 2023 · 2023
Closest in time.
The science of detecting llm-generated texts
Ruixiang Tang, Yu-Neng Chuang, and Xia Hu. 2023 · 2023
Closest in time.
Detection of Fake Generated Scientific Abstracts
Panagiotis C Theocharopoulos, Panagiotis Anagnostou, Anastasia Tsoukala, Spiros V Georgakopoulos, Sotiris K Tasoulis, and Vassilis P Plagianakos. 2023 · 2023
Closest in time.
ChatLog: Recording and Analyzing ChatGPT Across Time
Shangqing Tu, Chunyang Li, Jifan Yu, Xiaozhi Wang, Lei Hou, and Juanzi Li. 2023 · 2023
Closest in time.
Bot or Human? Detecting ChatGPT Imposters with A Single Question
Hong Wang, Xuan Luo, Weizhi Wang, and Xifeng Yan. 2023a · 2023
Closest in time.
Self-Critique Prompting with Large Language Models for Inductive Instructions
Rui Wang, Hongru Wang, Fei Mi, Yi Chen, Ruifeng Xu, and Kam-Fai Wong. 2023c · 2023
Closest in time.
M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection
Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Chenxi Whitehouse, Osama Mohammed Afzal, Tarek Mahmoud, Alham Fikri Aji, et al · 2023
Closest in time.
A prompt pattern catalog to enhance prompt engineering with chatgpt
Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C Schmidt. 2023 · 2023
Closest in time.
ChatGPT at universities–the least of our concerns
Jurgen Willems. 2023 · 2023
Closest in time.