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The recent large language models (LLMs), e.g., ChatGPT, have been able to generate human-like and fluent responses when provided with specific instructions.
Language models are few-shot learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 1901
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Optimal thresholding of classifiers to maximize F1 measure
Lipton, Z. C.; Elkan, C.; and Naryanaswamy, B. 2014 · 2014
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FaceNet: A Unified Embedding for Face Recognition and Clustering
Schroff, F.; Kalenichenko, D.; and Philbin, J. 2015 · 2015
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Prototypical networks for few-shot learning
Snell, J.; Swersky, K.; and Zemel, R. 2017 · 2017
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TDNN: a two-stage deep neural network for prompt-independent automated essay scoring
Jin, C.; He, B.; Hui, K.; and Sun, L. 2018 · 2018
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Evaluation of sentence embeddings in downstream and linguistic probing tasks
Perone, C. S.; Silveira, R.; and Paula, T. S. 2018 · 2018
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Improving language understanding by generative pre-training
Radford, A.; Narasimhan, K.; Salimans, T.; Sutskever, I.; et al. 2018 · 2018
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An LSTM approach to short text sentiment classification with word embeddings
Wang, J.-H.; Liu, T.-W.; Luo, X.; and Wang, L. 2018 · 2018
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Towards understanding and detecting fake reviews in app stores
Martens, D.; and Maalej, W. 2019 · 2019
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Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I.; et al. 2019 · 2019
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Defending against neural fake news
Zellers, R.; Holtzman, A.; Rashkin, H.; Bisk, Y.; Farhadi, A.; Roesner, F.; and Choi, Y. 2019 · 2019
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RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
Gehman, S.; Gururangan, S.; Sap, M.; Choi, Y.; and Smith, N. A. 2020 · 2020
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Automatic Detection of Generated Text is Easiest when Humans are Fooled
Ippolito, D.; Duckworth, D.; Callison-Burch, C.; and Eck, D. 2020 · 2020
Cited alongside, same era.
Automatic Detection of Machine Generated Text: A Critical Survey
Jawahar, G.; Abdul-Mageed, M.; and Laks Lakshmanan, V. 2020 · 2020
Cited alongside, same era.
A survey on contextual embeddings
Liu, Q.; Kusner, M. J.; and Blunsom, P. 2020 · 2020
Cited alongside, same era.
Authorship Attribution for Neural Text Generation
Uchendu, A.; Le, T.; Shu, K.; and Lee, D. 2020 · 2020
Cited alongside, same era.
Persistent anti-muslim bias in large language models
Abid, A.; Farooqi, M.; and Zou, J. 2021 · 2021
Cited alongside, same era.
The impact of multiple parallel phrase suggestions on email input and composition behaviour of native and non-native english writers
Buschek, D.; Zürn, M.; and Eiband, M. 2021 · 2021
Coauthor: Designing a human-ai collaborative writing dataset for exploring language model capabilities
Lee, M.; Liang, P.; and Yang, Q. 2022 · 2022
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ChatGPT: Future directions and open possibilities
Aljanabi, M. 2023 · 2023
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Do Large Language Models Understand Chemistry? A Conversation with ChatGPT
Castro Nascimento, C. M.; and Pimentel, A. S. 2023 · 2023
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Chatgpt goes to law school
Choi, J. H.; Hickman, K. E.; Monahan, A.; and Schwarcz, D. 2023 · 2023
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Real or fake text?: Investigating human ability to detect boundaries between human-written and machine-generated text
Dugan, L.; Ippolito, D.; Kirubarajan, A.; Shi, S.; and Callison-Burch, C. 2023 · 2023
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The role of ChatGPT in data science: how ai-assisted conversational interfaces are revolutionizing the field
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Cited alongside, same era.
All That’s ‘Human’Is Not Gold: Evaluating Human Evaluation of Generated Text
Clark, E.; August, T.; Serrano, S.; Haduong, N.; Gururangan, S.; and Smith, N. A. 2021 · 2021
Cited alongside, same era.
TweepFake: About detecting deepfake tweets
Fagni, T.; Falchi, F.; Gambini, M.; Martella, A.; and Tesconi, M. 2021 · 2021
Cited alongside, same era.
Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification
Wang, Y.; Wang, S.; Yao, Q.; and Dou, D. 2021 · 2021
Cited alongside, same era.
Ethical and social risks of harm from language models
Weidinger, L.; Mellor, J.; Rauh, M.; Griffin, C.; Uesato, J.; Huang, P.-S.; Cheng, M.; Glaese, M.; Balle, B.; Kasirzadeh, A.; et al. 2021 · 2021
Cited alongside, same era.
How human is human evaluation? Improving the gold standard for NLG with utility theory
Ethayarajh, K.; and Jurafsky, D. 2022 · 2022
Cited alongside, same era.
Translating radiology reports into plain language using ChatGPT and GPT-4 with prompt learning: results, limitations, and potential
Lyu, Q.; Tan, J.; Zapadka, M. E.; Ponnatapura, J.; Niu, C.; Myers, K. J.; Wang, G.; and Whitlow, C. T. 2023a
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Hassani, H.; and Silva, E. S. 2023 · 2023
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Artificial general intelligence (AGI) for education
Latif, E.; Mai, G.; Nyaaba, M.; Wu, X.; Liu, N.; Lu, G.; Li, S.; Liu, T.; and Zhai, X. 2023 · 2023
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Can large language models write reflectively
Li, Y.; Sha, L.; Yan, L.; Lin, J.; Raković, M.; Galbraith, K.; Lyons, K.; Gašević, D.; and Chen, G. 2023 · 2023
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Is this abstract generated by ai? a research for the gap between ai-generated scientific text and human-written scientific text
Ma, Y.; Liu, J.; and Yi, F. 2023 · 2023
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Detectgpt: Zero-shot machine-generated text detection using probability curvature
Mitchell, E.; Lee, Y.; Khazatsky, A.; Manning, C. D.; and Finn, C. 2023 · 2023
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Evaluating Reading Comprehension Exercises Generated by LLMs: A Showcase of ChatGPT in Education Applications
Xiao, C.; Xu, S. X.; Zhang, K.; Wang, Y.; and Xia, L. 2023 · 2023
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