  A consistent body of evidence suggests that dream reports significantly vary
from other types of textual transcripts with respect to semantic content.
Furthermore, it appears to be a widespread belief in the dream/sleep research
community that dream reports constitute rather ``unique'' strings of text. This
might be a notable issue for the growing amount of approaches using natural
language processing (NLP) tools to automatically analyse dream reports, as they
largely rely on neural models trained on non-dream corpora scraped from the
web. In this work, I will adopt state-of-the-art (SotA) large language models
(LLMs), to study if and how dream reports deviate from other human-generated
text strings, such as Wikipedia. Results show that, taken as a whole, DreamBank
does not deviate from Wikipedia. Moreover, on average, single dream reports are
significantly more predictable than Wikipedia articles. Preliminary evidence
suggests that word count, gender, and visual impairment can significantly shape
how predictable a dream report can appear to the model.
