Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain,
D., Perez, E., Schiefer, N., Dodds, Z. H., DasSarma, N.,
Tran-Johnson, E., et al. Language models (mostly) know
what they know. arXiv preprint 2207.05221, 2022.

Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B.,
Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and
Amodei, D. Scaling laws for neural language models.
arXiv preprint 2001.08361, 2020.

Kenton, Z., Everitt, T., Weidinger, L., Gabriel, I., Mikulik,
V., and Irving, G. Alignment of language agents. arXiv
preprint 2103.14659, 2021.

Klein, E. This changes everything. New York Times, 2023.
URL https://www.nytimes.com/2023/03/
12/opinion/chatbots-artificial-intel
ligence-future-weirdness.html.

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y.
Large language models are zero-shot reasoners. In ICML
2022 Workshop on Knowledge Retrieval and Language
Models, 2022. URL https://openreview.net/f
orum?id=6p3AuaHAFiN.
Eight Things to Know about Large Language Models