@article{korngiebel2021considering,
  title={Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery},
  author={Korngiebel, Diane M and Mooney, Sean D},
  journal={NPJ Digital Medicine},
  volume={4},
  number={1},
  pages={1--3},
  year={2021},
  publisher={Nature Publishing Group}
}

@article{binz2022using,
  title={Using cognitive psychology to understand GPT-3},
  author={Binz, Marcel and Schulz, Eric},
  journal={arXiv preprint arXiv:2206.14576},
  year={2022}
}

@article{stevenson2022putting,
  title={Putting GPT-3's Creativity to the (Alternative Uses) Test},
  author={Stevenson, Claire and Smal, Iris and Baas, Matthijs and Grasman, Raoul and van der Maas, Han},
  journal={arXiv preprint arXiv:2206.08932},
  year={2022}
}

@article{garg2018word,
  title={Word embeddings quantify 100 years of gender and ethnic stereotypes},
  author={Garg, Nikhil and Schiebinger, Londa and Jurafsky, Dan and Zou, James},
  journal={Proceedings of the National Academy of Sciences},
  volume={115},
  number={16},
  pages={E3635--E3644},
  year={2018},
  publisher={National Acad Sciences}
}

@inproceedings{bender2021dangers,
  title={On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?},
  author={Bender, Emily M and Gebru, Timnit and McMillan-Major, Angelina and Shmitchell, Shmargaret},
  booktitle={Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency},
  pages={610--623},
  year={2021}
}

@article{dale2021gpt,
  title={GPT-3: What’s it good for?},
  author={Dale, Robert},
  journal={Natural Language Engineering},
  volume={27},
  number={1},
  pages={113--118},
  year={2021},
  publisher={Cambridge University Press}
}

@article{brown2020language,
  title={Language models are few-shot learners},
  author={Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and others},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={1877--1901},
  year={2020}
}

@article{floridi2020gpt,
  title={GPT-3: Its nature, scope, limits, and consequences},
  author={Floridi, Luciano and Chiriatti, Massimo},
  journal={Minds and Machines},
  volume={30},
  number={4},
  pages={681--694},
  year={2020},
  publisher={Springer}
}


@article{rahwan2019machine,
  title={Machine behaviour},
  author={Rahwan, Iyad and Cebrian, Manuel and Obradovich, Nick and Bongard, Josh and Bonnefon, Jean-Fran{\c{c}}ois and Breazeal, Cynthia and Crandall, Jacob W and Christakis, Nicholas A and Couzin, Iain D and Jackson, Matthew O and others},
  journal={Nature},
  volume={568},
  number={7753},
  pages={477--486},
  year={2019},
  publisher={Nature Publishing Group}
}

@article{black2022gpt,
  title={Gpt-neox-20b: An open-source autoregressive language model},
  author={Black, Sid and Biderman, Stella and Hallahan, Eric and Anthony, Quentin and Gao, Leo and Golding, Laurence and He, Horace and Leahy, Connor and McDonell, Kyle and Phang, Jason and others},
  journal={arXiv preprint arXiv:2204.06745},
  year={2022}
}


@article{miotto_who_2022,
	title = {Who is {GPT}-3? {An} {Exploration} of {Personality}, {Values} and {Demographics}},
	shorttitle = {Who is {GPT}-3?},
	url = {http://arxiv.org/abs/2209.14338},
	doi = {10.48550/arXiv.2209.14338},
	abstract = {Language models such as GPT-3 have caused a furore in the research community. Some studies found that GPT-3 has some creative abilities and makes mistakes that are on par with human behaviour. This paper answers a related question: who is GPT-3? We administered two validated measurement tools to GPT-3 to assess its personality, the values it holds and its self-reported demographics. Our results show that GPT-3 scores similarly to human samples in terms of personality and - when provided with a model response memory - in terms of the values it holds. We provide the first evidence of psychological assessment of the GPT-3 model and thereby add to our understanding of the GPT-3 model. We close with suggestions for future research that moves social science closer to language models and vice versa.},
	urldate = {2022-10-03},
	author = {Miotto, Marilù and Rossberg, Nicola and Kleinberg, Bennett},
	month = sep,
	year = {2022},
	note = {arXiv:2209.14338 [cs]},
	keywords = {Computer Science - Computation and Language},
	file = {arXiv.org Snapshot:/Users/bennettkleinberg/Zotero/storage/6NCPEUG4/2209.html:text/html},
}

@article{shihadehbrilliance,
  title={Brilliance Bias in GPT-3},
  author={Shihadeh, Juliana and Ackerman, Margareta and Troske, Ashley and Lawson, Nicole and Gonzalez, Edith},
  year={2022}
}

@article{vandermaas2021,
	title = {How much intelligence is there in artificial intelligence? A 2020 update},
	author = {van der Maas, Han L.J. and Snoek, Lukas and Stevenson, Claire E.},
	year = {2021},
	month = {07},
	date = {2021-07},
	journal = {Intelligence},
	pages = {101548},
	volume = {87},
	doi = {10.1016/j.intell.2021.101548},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0160289621000325},
	langid = {en}
}