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ChatGPT has gained a huge popularity since its introduction.
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris 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 · 1901
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Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
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Evaluating Predictive Uncertainty, Visual Objects Classification and Recognising Textual Entailment: Selected Proceedings of the First PASCAL Machine Learning Challenges Workshop
Joaquin Candela-Quinonero, Ido Dagan, Bernardo Magnini, and Florence d’Alché Buc. 2006 · 2006
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Cognitive mechanisms for transitive inference performance in rhesus monkeys: Measuring the influence of associative strength and inferred order
Regina Paxton Gazes, Nicholas W. Chee, and Robert R. Hampton. 2012 · 2012
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
Almost human: Anthropomorphism increases trust resilience in cognitive agents
Ewart J. De Visser, Samuel S. Monfort, Ryan McKendrick, Melissa A. B. Smith, Patrick E. McKnight, Frank Krueger, and Raja Parasuraman. 2016 · 2016
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A distributional study of negated adjectives and antonyms
Laura Aina, Raffaella Bernardi, and Raquel Fernández. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Neural correlates of variations in human trust in human-like machines during non-reciprocal interactions
Eun-Soo Jung, Suh-Yeon Dong, and Soo-Young Lee. 2019 · 2019
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A logic-driven framework for consistency of neural models
Tao Li, Vivek Gupta, Maitrey Mehta, and Vivek Srikumar. 2019 · 2019
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WiC: the word-in-context dataset for evaluating context-sensitive meaning representations
Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019 · 2019
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Energy and policy considerations for deep learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
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What if we simply swap the two text fragments? A straightforward yet effective way to test the robustness of methods to confounding signals in nature language inference tasks
Haohan Wang, Da Sun, and Eric P. Xing. 2019 · 2019
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Logic-guided data augmentation and regularization for consistent question answering
Akari Asai and Hannaneh Hajishirzi. 2020 · 2020
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Make up your mind! Adversarial generation of inconsistent natural language explanations
Oana-Maria Camburu, Brendan Shillingford, Pasquale Minervini, Thomas Lukasiewicz, and Phil Blunsom. 2020 · 2020
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ELECTRA: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020 · 2020
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What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models
Allyson Ettinger. 2020 · 2020
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An analysis of natural language inference benchmarks through the lens of negation
Md Mosharaf Hossain, Venelin Kovatchev, Pranoy Dutta, Tiffany Kao, Elizabeth Wei, and Eduardo Blanco. 2020 · 2020
Earlier work this paper cites.
A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, and Xinping Yi. 2020 · 2020
Cited alongside, same era.
How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
Cited alongside, same era.
Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly
Nora Kassner and Hinrich Schütze. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Cited alongside, same era.
On the systematicity of probing contextualized word representations: The case of hypernymy in BERT
Abhilasha Ravichander, Eduard Hovy, Kaheer Suleman, Adam Trischler, and Jackie Chi Kit Cheung. 2020 · 2020
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Later among the works it cites.
Measuring reliability of large language models through semantic consistency
Harsh Raj, Domenic Rosati, and Subhabrata Majumdar. 2022 · 2022
Later among the works it cites.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2022 · 2022
Later among the works it cites.
ChatGPT goes to law school
Jonathan H. Choi, Kristin E. Hickman, Amy Monahan, and Daniel B. Schwarcz. 2023 · 2023
Closest in time.
ChatGPT could be a game-changer for marketers, but it won’t replace humans any time soon
Omar H. Fares. 2023 · 2023
Closest in time.
Mathematical capabilities of ChatGPT
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On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
Cited alongside, same era.
Erratum: Measuring and improving consistency in pretrained language models
Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, and Yoav Goldberg. 2021 · 2021
Cited alongside, same era.
Understanding by understanding not: Modeling negation in language models
Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R Devon Hjelm, Alessandro Sordoni, and Aaron Courville. 2021 · 2021
Cited alongside, same era.
Learn to resolve conversational dependency: A consistency training framework for conversational question answering
Gangwoo Kim, Hyunjae Kim, Jungsoo Park, and Jaewoo Kang. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Cited alongside, same era.
Unsupervised paraphrasing consistency training for low resource named entity recognition
Rui Wang and Ricardo Henao. 2021 · 2021
Cited alongside, same era.
Consistency regularization for cross-lingual fine-tuning
Bo Zheng, Li Dong, Shaohan Huang, Wenhui Wang, Zewen Chi, Saksham Singhal, Wanxiang Che, Ting Liu, Xia Song, and Furu Wei. 2021 · 2021
Cited alongside, same era.
Simon Frieder, Luca Pinchetti, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Christian Petersen, Alexis Chevalier, and Julius Berner. 2023 · 2023
Closest in time.
KNOW how to make up your mind! adversarially detecting and alleviating inconsistencies in natural language explanations
Myeongjun Jang, Bodhisattwa Prasad Majumder, Julian McAuley, Thomas Lukasiewicz, and Oana-Maria Camburu. 2023 · 2023
Closest in time.
Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models
Tiffany H. Kung, Morgan Cheatham, Arielle Medenilla, Czarina Sillos, Lorie De Leon, Camille Elepaño, Maria Madriaga, Rimel Aggabao, Giezel Diaz-Candido, James Maningo, et al. 2023 · 2023
Closest in time.
ChatGPT reaches 100 million users two months after launch
Dan Milmo. 2023 · 2023
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‘ChatGPT needs a huge amount of editing’: users’ views mixed on AI chatbot
Clea Skopeliti and Dan Milmo. 2023 · 2023
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Evaluating the factual consistency of large language models through news summarization
Derek Tam, Anisha Mascarenhas, Shiyue Zhang, Sarah Kwan, Mohit Bansal, and Colin Raffel. 2023 · 2023
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Would ChatGPT get a Wharton MBA? A prediction based on its performance in the operations management course
Christian Terwiesch. 2023 · 2023
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SCOTT: Self-consistent chain-of-thought distillation
Peifeng Wang, Zhengyang Wang, Zheng Li, Yifan Gao, Bing Yin, and Xiang Ren. 2023 · 2023
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The future of ChatGPT in academic research and publishing: A commentary for clinical and translational medicine
Jun Wen and Wei Wang. 2023 · 2023
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Ruochen Zhao, Xingxuan Li, Yew Ken Chia, Bosheng Ding, and Lidong Bing. 2023 · 2023
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Red teaming chatgpt via jailbreaking: Bias, robustness, reliability and toxicity
Terry Yue Zhuo, Yujin Huang, Chunyang Chen, and Zhenchang Xing. 2023 · 2023
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Beyond distributional hypothesis: Let language models learn meaning-text correspondence
Myeongjun Jang, Frank Mtumbuka, and Thomas Lukasiewicz. 2022b · 2042
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