ple tasks(Hendrycks et al., 2020; Bai et al., 2022a; Chung
et al., 2022; Ganguli et al., 2023). In another important
way, though, the problem is becoming more difﬁcult: More
capable models can better recognize the speciﬁc circum-
stances under which they are trained. Because of this, they
are more likely to learn to act as expected in precisely those
circumstances while behaving competently but unexpect-
edly in others. This can surface in the form of problems that
Perez et al. (2022) call sycophancy, where a model answers
subjective questions in a way that ﬂatters their user’s stated
beliefs, and sandbagging, where models are more likely to
endorse common misconceptions when their user appears
to be less educated. It seems likely that issues like these
played some role in the bizarre, manipulative behavior that
early versions of Microsoft Bing Chat showed, despite the
system having been tested extensively before launch (Roose,
2023; Perrigo, 2023; Mehdi, 2023).
Eight Things to Know about Large Language Models