These results are in tension, at least to some extent, with
the common intuition that LLMs are nothing but statistical
next-word predictors, and therefore cannot learn or reason
about anything but text. While the premise of this intuition
is technically correct in some cases, it can paint a misleading
picture of the often-rich representations of the world that
LLMs develop as they are trained. In addition, LLMs are
increasingly often augmented with other ways of learning
about the world that make this claim literally false, such as
through interactive training methods (Ziegler et al., 2019;
Stiennon et al., 2020; Ouyang et al., 2022; Bai et al., 2022a),
integration with image processing systems, (Alayrac et al.,
2022; OpenAI, 2023b), or integration with other software
tools (Nakano et al., 2021; Menick et al., 2022; Collins et al.,
2022; Schick et al., 2023; OpenAI, 2023a).

4. There are no reliable techniques for

steering the behavior of LLMs
Eight Things to Know about Large Language Models